title = {Features of electronic Early Warning systems which impact clinical decision making {\textbar} {IEEE} Conference Publication {\textbar} {IEEE} Xplore},
file = {Features of electronic Early Warning systems which impact clinical decision making | IEEE Conference Publication | IEEE Xplore:/home/ulinja/Zotero/storage/Q9BI6RWR/6266394.html:text/html;Features of electronic Early Warning systems which.pdf:/home/ulinja/Zotero/storage/SSHGFSTF/Features of electronic Early Warning systems which.pdf:application/pdf},
abstract = {The world has been facing the challenge of {COVID}-19 since the end of 2019. It is expected that the world will need to battle the {COVID}-19 pandemic with precautious measures, until an effective vaccine is developed. This paper proposes a real-time {COVID}-19 detection and monitoring system. The proposed system would employ an Internet of Things ({IoTs}) framework to collect real-time symptom data from users to early identify suspected coronaviruses cases, to monitor the treatment response of those who have already recovered from the virus, and to understand the nature of the virus by collecting and analyzing relevant data. The framework consists of five main components: Symptom Data Collection and Uploading (using wearable sensors), Quarantine/Isolation Center, Data Analysis Center (that uses machine learning algorithms), Health Physicians, and Cloud Infrastructure. To quickly identify potential coronaviruses cases from this real-time symptom data, this work proposes eight machine learning algorithms, namely Support Vector Machine ({SVM}), Neural Network, Naïve Bayes, K-Nearest Neighbor (K-{NN}), Decision Table, Decision Stump, {OneR}, and {ZeroR}. An experiment was conducted to test these eight algorithms on a real {COVID}-19 symptom dataset, after selecting the relevant symptoms. The results show that five of these eight algorithms achieved an accuracy of more than 90 \%. Based on these results we believe that real-time symptom data would allow these five algorithms to provide effective and accurate identification of potential cases of {COVID}-19, and the framework would then document the treatment response for each patient who has contracted the virus.},
pages = {102149},
journaltitle = {Biomedical Signal Processing and Control},
shortjournal = {Biomedical Signal Processing and Control},
author = {Otoom, Mwaffaq and Otoum, Nesreen and Alzubaidi, Mohammad A. and Etoom, Yousef and Banihani, Rudaina},
urldate = {2023-04-27},
date = {2020-09-01},
langid = {english},
keywords = {Coronaviruses, {COVID}-19, Early identification or prediction, Internet of Things, Real-time monitoring, Treatment response},
file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/NCS9RXIF/Otoom et al. - 2020 - An IoT-based framework for early identification an.pdf:application/pdf;ScienceDirect Snapshot:/home/ulinja/Zotero/storage/ZS8ARH8Q/S1746809420302949.html:text/html},
abstract = {{NEWS}2 is the latest version of the National Early Warning Score ({NEWS}), first produced in 2012 and updated in December 2017, which advocates a system to standardise the assessment and response to acute illness.},
title = {The ability of the National Early Warning Score ({NEWS}) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death},
volume = {84},
issn = {1873-1570},
doi = {10.1016/j.resuscitation.2012.12.016},
abstract = {{INTRODUCTION}: Early warning scores ({EWS}) are recommended as part of the early recognition and response to patient deterioration. The Royal College of Physicians recommends the use of a National Early Warning Score ({NEWS}) for the routine clinical assessment of all adult patients.
{METHODS}: We tested the ability of {NEWS} to discriminate patients at risk of cardiac arrest, unanticipated intensive care unit ({ICU}) admission or death within 24h of a {NEWS} value and compared its performance to that of 33 other {EWSs} currently in use, using the area under the receiver-operating characteristic ({AUROC}) curve and a large vital signs database (n=198,755 observation sets) collected from 35,585 consecutive, completed acute medical admissions.
{RESULTS}: The {AUROCs} (95\% {CI}) for {NEWS} for cardiac arrest, unanticipated {ICU} admission, death, and any of the outcomes, all within 24h, were 0.722 (0.685-0.759), 0.857 (0.847-0.868), 0.894 (0.887-0.902), and 0.873 (0.866-0.879), respectively. Similarly, the ranges of {AUROCs} (95\% {CI}) for the other 33 {EWSs} were 0.611 (0.568-0.654) to 0.710 (0.675-0.745) (cardiac arrest); 0.570 (0.553-0.568) to 0.827 (0.814-0.840) (unanticipated {ICU} admission); 0.813 (0.802-0.824) to 0.858 (0.849-0.867) (death); and 0.736 (0.727-0.745) to 0.834 (0.826-0.842) (any outcome).
{CONCLUSIONS}: {NEWS} has a greater ability to discriminate patients at risk of the combined outcome of cardiac arrest, unanticipated {ICU} admission or death within 24h of a {NEWS} value than 33 other {EWSs}.},
pages = {465--470},
number = {4},
journaltitle = {Resuscitation},
shortjournal = {Resuscitation},
author = {Smith, Gary B. and Prytherch, David R. and Meredith, Paul and Schmidt, Paul E. and Featherstone, Peter I.},
date = {2013-04},
pmid = {23295778},
keywords = {Aged, Early Diagnosis, Female, Heart Arrest, Hospital Mortality, Humans, Intensive Care Units, Male, Patient Admission, Risk Assessment, {ROC} Curve, Severity of Illness Index, United Kingdom, Vital Signs},
file = {Accepted Version:/home/ulinja/Zotero/storage/WKEEUEAW/Smith et al. - 2013 - The ability of the National Early Warning Score (N.pdf:application/pdf},
title = {Two Algorithms for Detecting Respiratory Rate from {ECG} Signal},
isbn = {978-3-540-36841-0},
doi = {10.1007/978-3-540-36841-0_1030},
series = {{IFMBE} Proceedings},
abstract = {Wearable real-time health monitoring technology has been developed for remote diagnosis and health check during daily life. The present study proposes two algorithms to detect respiratory rate from {ECG} signal. One gets respiratory rate by measuring the number of {ECG} samples in R-R interval and its advantage lies in its simplicity. The other detects the rate by measuring the size of R wave in {QRS} signal. This algorithm can detect the rate more robustly but it is complicated and requires the {ECG} signal base line to be stabilized. The preliminary study in laboratory environment showed that the precision of these algorithms was over 97\%.},
pages = {4069--4071},
booktitle = {World Congress on Medical Physics and Biomedical Engineering 2006},
file = {Kim et al. - 2007 - Two Algorithms for Detecting Respiratory Rate from.pdf:/home/ulinja/Zotero/storage/YNEGUM7M/Kim et al. - 2007 - Two Algorithms for Detecting Respiratory Rate from.pdf:application/pdf},
title = {Validation of a modified Early Warning Score in medical admissions},
volume = {94},
issn = {1460-2725},
url = {https://doi.org/10.1093/qjmed/94.10.521},
doi = {10.1093/qjmed/94.10.521},
abstract = {The Early Warning Score ({EWS}) is a simple physiological scoring system suitable for bedside application. The ability of a modified Early Warning Score ({MEWS}) to identify medical patients at risk of catastrophic deterioration in a busy clinical area was investigated. In a prospective cohort study, we applied {MEWS} to patients admitted to the 56‐bed acute Medical Admissions Unit ({MAU}) of a District General Hospital ({DGH}). Data on 709 medical emergency admissions were collected during March 2000. Main outcome measures were death, intensive care unit ({ICU}) admission, high dependency unit ({HDU}) admission, cardiac arrest, survival and hospital discharge at 60 days. Scores of 5 or more were associated with increased risk of death ({OR} 5.4, 95\%{CI} 2.8–10.7), {ICU} admission ({OR} 10.9, 95\%{CI} 2.2–55.6) and {HDU} admission ({OR} 3.3, 95\%{CI} 1.2–9.2). {MEWS} can be applied easily in a {DGH} medical admission unit, and identifies patients at risk of deterioration who require increased levels of care in the {HDU} or {ICU}. A clinical pathway could be created, using nurse practitioners and/or critical care physicians, to respond to high scores and intervene with appropriate changes in clinical management.},
pages = {521--526},
number = {10},
journaltitle = {{QJM}: An International Journal of Medicine},
shortjournal = {{QJM}: An International Journal of Medicine},
author = {Subbe, C.P. and Kruger, M. and Rutherford, P. and Gemmel, L.},
urldate = {2023-04-30},
date = {2001-10-01},
file = {Full Text PDF:/home/ulinja/Zotero/storage/P7TJ5DJB/Subbe et al. - 2001 - Validation of a modified Early Warning Score in me.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/FFJJTX3I/1558977.html:text/html},
title = {Pre-hospital National Early Warning Score ({NEWS}) is associated with in-hospital mortality and critical care unit admission: A cohort study},
shorttitle = {Pre-hospital National Early Warning Score ({NEWS}) is associated with in-hospital mortality and critical care unit admission},
abstract = {Background
National Early Warning Score ({NEWS}) is increasingly used in {UK} hospitals. However, there is only limited evidence to support the use of pre-hospital early warning scores. We hypothesised that pre-hospital {NEWS} was associated with death or critical care escalation within the first 48h of hospital stay.
Methods
Planned secondary analysis of a prospective cohort study at a single {UK} teaching hospital. Consecutive medical ward admissions over a 20-day period were included in the study. Data were collected from ambulance report forms, medical notes and electronic patient records. Pre-hospital {NEWS} was calculated retrospectively. The primary outcome was a composite of death or critical care unit escalation within 48h of hospital admission. The secondary outcome was length of hospital stay.
Results
189 patients were included in the analysis. The median pre-hospital {NEWS} was 3 ({IQR} 1–5). 13 patients (6.9\%) died or were escalated to the critical care unit within 48h of hospital admission. Pre-hospital {NEWS} was associated with death or critical care unit escalation ({OR}, 1.25; 95\% {CI}, 1.04–1.51; p=0.02), but {NEWS} on admission to hospital was more strongly associated with this outcome ({OR}, 1.52; 95\% {CI}, 1.18–1.97, p{\textless}0.01). Neither was associated with hospital length of stay.
Conclusion
Pre-hospital {NEWS} was associated with death or critical care unit escalation within 48h of hospital admission. {NEWS} could be used by ambulance crews to assist in the early triage of patients requiring hospital treatment or rapid transport. Further cohort studies or trials in large samples are required before implementation.},
pages = {17--21},
journaltitle = {Annals of Medicine and Surgery},
shortjournal = {Annals of Medicine and Surgery},
author = {Abbott, Tom E. F. and Cron, Nicholas and Vaid, Nidhi and Ip, Dorothy and Torrance, Hew D. T. and Emmanuel, Julian},
urldate = {2023-04-28},
date = {2018-03-01},
langid = {english},
keywords = {Acute care emergency ambulance systems, Clinical research, Intensive care, Pre-hospital},
file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/2HPZCFXG/Abbott et al. - 2018 - Pre-hospital National Early Warning Score (NEWS) i.pdf:application/pdf},
abstract = {The early warning score can help to prevent, recognize and act at the first signs of clinical and physiological deterioration. The objective of this study is to evaluate different scales for use in the prehospital setting and to select the most relevant one by applicability and capacity to predict mortality in the first 48h. A prospective longitudinal observational study was conducted in patients over 18years of age who were treated by the advanced life support unit and transferred to the emergency department between April and July 2018. We analyzed demographic variables as well as the physiological parameters and clinical observations necessary to complement the {EWS}. Subsequently, each patient was followed up, considering their final diagnosis and mortality data. A total of 349 patients were included in our study. Early mortality before the first 48h affected 27 patients (7.7\%). The scale with the best capacity to predict early mortality was the National Early Warning Score 2, with an area under the curve of 0.896 (95\% {CI} 0.82–0.97). The score with the lowest global classification error was 10 points with sensitivity of 81.5\% (95\% {CI} 62.7–92.1) and specificity of 88.5\% (95\% {CI} 84.5–91.6). The early warning score studied (except modified early warning score) shows no statistically significant differences between them; however, the National Early Warning Score 2 is the most used score internationally, validated at the prehospital scope and with a wide scientific literature that supports its use. The Prehospital Emergency Medical Services should include this scale among their operative elements to complement the structured and objective evaluation of the critical patient.},
pages = {581--589},
number = {4},
journaltitle = {Internal and Emergency Medicine},
shortjournal = {Intern Emerg Med},
author = {Martín-Rodríguez, Francisco and Castro-Villamor, Miguel Ángel and del Pozo Vegas, Carlos and Martín-Conty, José Luis and Mayo-Iscar, Agustín and Delgado Benito, Juan Francisco and del Brio Ibañez, Pablo and Arnillas-Gómez, Pedro and Escudero-Cuadrillero, Carlos and López-Izquierdo, Raúl},
urldate = {2023-04-28},
date = {2019-06-01},
langid = {english},
keywords = {Clinical research, Early mortality, Early warning score, Prehospital care, Prognosis},
file = {Full Text PDF:/home/ulinja/Zotero/storage/2LVIYDZR/Martín-Rodríguez et al. - 2019 - Analysis of the early warning score to detect crit.pdf:application/pdf},
title = {Predicting in-hospital mortality in adult non-traumatic emergency department patients: a retrospective comparison of the Modified Early Warning Score ({MEWS}) and machine learning approach},
volume = {9},
issn = {2167-8359},
url = {https://peerj.com/articles/11988},
doi = {10.7717/peerj.11988},
shorttitle = {Predicting in-hospital mortality in adult non-traumatic emergency department patients},
abstract = {Background A feasible and accurate risk prediction systems for emergency department ({ED}) patients is urgently required. The Modified Early Warning Score ({MEWS}) is a wide-used tool to predict clinical outcomes in {ED}. Literatures showed that machine learning ({ML}) had better predictability in specific patient population than traditional scoring system. By analyzing a large multicenter dataset, we aim to develop a {ML} model to predict in-hospital morality of the adult non traumatic {ED} patients for different time stages, and comparing performance with other {ML} models and {MEWS}. Methods A retrospective observational cohort study was conducted in five Taiwan {EDs} including two tertiary medical centers and three regional hospitals. All consecutively adult ({\textgreater}17 years old) non-traumatic patients admit to {ED} during a 9-year period (January first, 2008 to December 31th, 2016) were included. Exclusion criteria including patients with (1) out-of-hospital cardiac arrest and (2) discharge against medical advice and transferred to other hospital (3) missing collect variables. The primary outcome was in-hospital mortality and were categorized into 6, 24, 72, 168 hours mortality. {MEWS} was calculated by systolic blood pressure, pulse rate, respiratory rate, body temperature, and level of consciousness. An ensemble supervised stacking {ML} model was developed and compared to sensitive and unsensitive Xgboost, Random Forest, and Adaboost. We conducted a performance test and examine both the area under the receiver operating characteristic ({AUROC}) and the area under the precision and recall curve ({AUPRC}) as the comparative measures. Result After excluding 182,001 visits (7.46\%), study group was consisted of 24,37,326 {ED} visits. The dataset was split into 67\% training data and 33\% test data for {ML} model development. There was no statistically difference found in the characteristics between two groups. For the prediction of 6, 24, 72, 168 hours in-hospital mortality, the {AUROC} of {MEW} and {ML} mode was 0.897, 0.865, 0.841, 0.816 and 0.939, 0.928, 0.913, 0.902 respectively. The stacking {ML} model outperform other {ML} model as well. For the prediction of in-hospital mortality over 48-hours, {AUPRC} performance of {MEWS} drop below 0.1, while the {AUPRC} of {ML} mode was 0.317 in 6 hours and 0.2150 in 168 hours. For each time frame, {ML} model achieved statistically significant higher {AUROC} and {AUPRC} than {MEWS} (all P {\textless} 0.001). Both models showed decreasing prediction ability as time elapse, but there was a trend that the gap of {AUROC} values between two model increases gradually (P {\textless} 0.001). Three {MEWS} thresholds (score {\textgreater}3, {\textgreater}4, and {\textgreater}5) were determined as baselines for comparison, {ML} mode consistently showed improved or equally performance in sensitivity, {PPV}, {NPV}, but not in specific. Conclusion Stacking {ML} methods improve predicted in-hospital mortality than {MEWS} in adult non-traumatic {ED} patients, especially in the prediction of delayed mortality.},
pages = {e11988},
journaltitle = {{PeerJ}},
shortjournal = {{PeerJ}},
author = {Wu, Kuan-Han and Cheng, Fu-Jen and Tai, Hsiang-Ling and Wang, Jui-Cheng and Huang, Yii-Ting and Su, Chih-Min and Chang, Yun-Nan},
urldate = {2023-04-28},
date = {2021-08-24},
langid = {english},
note = {Publisher: {PeerJ} Inc.},
file = {Full Text PDF:/home/ulinja/Zotero/storage/H2MPDP9A/Wu et al. - 2021 - Predicting in-hospital mortality in adult non-trau.pdf:application/pdf},
title = {Vitls Tego - Vitals monitoring device for infants},
url = {https://www.vitlsinc.com/unique-features},
abstract = {Our wearable medical device has tackled the downsides to current vital monitoring options and engineered the ultimate way to care for your patients without the hassle.},
abstract = {Equivital’s {LifeMonitor} is a body worn sensor which measures {ECG}, heart rate, breathing rate, skin temperature, activity and body position.},
shorttitle = {Evaluation and improvement of the National Early Warning Score ({NEWS}2) for {COVID}-19},
abstract = {The National Early Warning Score ({NEWS}2) is currently recommended in the {UK} for the risk stratification of {COVID}-19 patients, but little is known about its ability to detect severe cases. We aimed to evaluate {NEWS}2 for the prediction of severe {COVID}-19 outcome and identify and validate a set of blood and physiological parameters routinely collected at hospital admission to improve upon the use of {NEWS}2 alone for medium-term risk stratification.},
pages = {23},
number = {1},
journaltitle = {{BMC} Medicine},
shortjournal = {{BMC} Med},
author = {Carr, Ewan and Bendayan, Rebecca and Bean, Daniel and Stammers, Matt and Wang, Wenjuan and Zhang, Huayu and Searle, Thomas and Kraljevic, Zeljko and Shek, Anthony and Phan, Hang T. T. and Muruet, Walter and Gupta, Rishi K. and Shinton, Anthony J. and Wyatt, Mike and Shi, Ting and Zhang, Xin and Pickles, Andrew and Stahl, Daniel and Zakeri, Rosita and Noursadeghi, Mahdad and O’Gallagher, Kevin and Rogers, Matt and Folarin, Amos and Karwath, Andreas and Wickstrøm, Kristin E. and Köhn-Luque, Alvaro and Slater, Luke and Cardoso, Victor Roth and Bourdeaux, Christopher and Holten, Aleksander Rygh and Ball, Simon and {McWilliams}, Chris and Roguski, Lukasz and Borca, Florina and Batchelor, James and Amundsen, Erik Koldberg and Wu, Xiaodong and Gkoutos, Georgios V. and Sun, Jiaxing and Pinto, Ashwin and Guthrie, Bruce and Breen, Cormac and Douiri, Abdel and Wu, Honghan and Curcin, Vasa and Teo, James T. and Shah, Ajay M. and Dobson, Richard J. B.},
file = {Full Text PDF:/home/ulinja/Zotero/storage/4RTVXPRT/Carr et al. - 2021 - Evaluation and improvement of the National Early W.pdf:application/pdf},
title = {An {IoT}-Based Healthcare Platform for Patients in {ICU} Beds During the {COVID}-19 Outbreak},
volume = {9},
issn = {2169-3536},
doi = {10.1109/ACCESS.2021.3058448},
abstract = {There is a global concern with the escalating number of patients at hospitals caused mainly by population aging, chronic diseases, and recently by the {COVID}-19 outbreak. To smooth this challenge, {IoT} emerges as an encouraging paradigm because it provides the scalability required for this purpose, supporting continuous and reliable health monitoring on a global scale. Based on this context, an {IoT}-based healthcare platform to provide remote monitoring for patients in a critical situation was proposed in the authors’ previous works. Therefore, this paper aims to extend the platform by integrating wearable and unobtrusive sensors to monitor patients with coronavirus disease. Furthermore, we report a real deployment of our approach in an intensive care unit for {COVID}-19 patients in Brazil.},
pages = {27262--27277},
journaltitle = {{IEEE} Access},
author = {Filho, Itamir de Morais Barroca and Aquino, Gibeon and Malaquias, Ramon Santos and Girão, Gustavo and Melo, Sávio Rennan Menêzes},
date = {2021},
note = {Conference Name: {IEEE} Access},
keywords = {{COVID}-19, Internet of Things, Biomedical monitoring, Cloud computing, Healthcare, Medical services, Monitoring, platform, Protocols, remote monitoring, Sensors},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/QJRQD4DV/9351912.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/Z47T3IBP/Filho et al. - 2021 - An IoT-Based Healthcare Platform for Patients in I.pdf:application/pdf},
}
@article{gidari_predictive_2020,
title = {Predictive value of National Early Warning Score 2 ({NEWS}2) for intensive care unit admission in patients with {SARS}-{CoV}-2 infection},
abstract = {Background: From January 2020, Coronavirus disease 19 ({COVID}-19) has rapidly spread all over the world. An early assessment of illness severity is important for the stratification of patients. We analysed the predictive value of National Early Warning Score 2 ({NEWS}2) for intensive care unit admission ({ICU}) in patients with Severe Acute Respiratory Syndrome- Coronavirus-2 ({SARS}-{CoV}-2) infection.Methods: Data of 71 patients with {SARS}-{CoV}-2 admitted from 1 March to 20 April 2020, to the Clinic of Infectious Diseases of Perugia Hospital, Italy, were retrospectively reviewed. {NEWS}2 at hospital admission, demographic, comorbidity and clinical data were collected. Univariate and multivariate analyses were performed to establish the correlation between each variable and {ICU} admission.Results: Among 68 patients included in the analysis, 27 were admitted to {ICU}. {NEWS}2 at hospital admission was a good predictor of {ICU} admission as shown by an area under the receiver-operating characteristic curve analysis of 0.90 (standard error 0.04; 95\% confidence interval 0.82–0.97). In multivariate logistic regression analysis, {NEWS}2 was significantly related to {ICU} admission using thresholds of 5 and 7. No other clinical variables included in the model were significantly correlated with {ICU} admission.A {NEWS}2 threshold of 5 had higher sensitivity than a threshold of 7 (89\% and 63\%). Higher specificity, positive likelihood ratio and positive predictive value were found using a threshold of 7 than a threshold of 5.Conclusions: {NEWS}2 at hospital admission was a good predictor for {ICU} admission. Patients with severe {COVID}-19 were correctly and rapidly stratified.},
pages = {698--704},
number = {10},
journaltitle = {Infectious Diseases},
author = {Gidari, Anna and De Socio, Giuseppe Vittorio and Sabbatini, Samuele and Francisci, Daniela},
title = {National Early Warning Score ({NEWS}) as an emergency department predictor of disease severity and 90-day survival in the acutely dyspneic patient – a prospective observational study},
abstract = {National Early Warning Score ({NEWS}) was designed to detect deteriorating patients in hospital wards, specifically those at increased risk of {ICU} admission, cardiac arrest, or death within 24 h. {NEWS} is not validated for use in Emergency Departments ({ED}), but emerging data suggest it may be useful. A criticism of {NEWS} is that patients with chronic poor oxygenation, e.g. severe chronic obstructive pulmonary disease ({COPD}), will have elevated {NEWS} also in the absence of acute deterioration, possibly reducing the predictive power of {NEWS} in this subgroup. We wanted to prospectively evaluate the usefulness of {NEWS} in unselected adult patients emergently presenting in a Norwegian {ED} with respiratory distress as main symptom. In respiratory distressed patients, {NEWS} was calculated on {ED} arrival, after 2–4 h, and the next day. Manchester Triage Scale ({MTS}) category, age, gender, comorbidity ({ASA} score), {ICU}-admission, ventilatory support, and discharge diagnoses were noted. Survival status was tracked for {\textgreater}90 days through the Population Registry. Data are medians (25–75th percentiles). Factors predicting 90-day survival were analysed with multiple logistic regression. We included 246 patients; 71 years old (60–80), 89 \% home-dwelling, 74 \% {ASA} 3–4, 72 \% {MTS} 1–2, 88 \% admitted to hospital. {NEWS} on arrival was 5 (3–7). {NEWS} correlated closely with {MTS} category and maximum in-hospital level of care ({ED}, ward, high-dependency unit, {ICU}). Sixteen patients died in-hospital, 26 died after discharge within 90 days. Controlled for age, {ASA} score, and {COPD}, a higher {NEWS} on {ED} arrival predicted poorer 90-day survival. Increased {NEWS} also correlated with decreased 30-day- and in-hospital survival and a decreased probability for home-dwelling patients to be discharged directly home. In respiratory distressed patients, {NEWS} on {ED} arrival correlated closely with triage category and need of {ICU} admission and predicted long-term out-of-hospital survival controlled for age, comorbidity, and {COPD}. {NEWS} should be explored in the {ED} setting to determine its role in clinical decision-making and in communication along the acute care chain.},
pages = {1--8},
number = {1},
journaltitle = {Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine},
shortjournal = {Scand J Trauma Resusc Emerg Med},
author = {Bilben, Bente and Grandal, Linda and Søvik, Signe},
file = {Full Text PDF:/home/ulinja/Zotero/storage/YAGFBLNR/Bilben et al. - 2016 - National Early Warning Score (NEWS) as an emergenc.pdf:application/pdf},
Several triage systems have been developed for use in the emergency department ({ED}), however they are not designed to detect deterioration in patients. Deteriorating patients may be at risk of going undetected during their {ED} stay and are therefore vulnerable to develop serious adverse events ({SAEs}). The National Early Warning Score ({NEWS}) has a good ability to discriminate ward patients at risk of {SAEs}. The utility of {NEWS} had not yet been studied in an {ED}.
Objective
To explore the performance of the {NEWS} in an {ED} with regard to predicting adverse outcomes.
Design
A prospective observational study. Patients Eligible patients were those presenting to the {ED} during the 6 week study period with an Emergency Severity Index ({ESI}) of 2 and 3 not triaged to the resuscitation room.
Intervention
{NEWS} was documented at three time points: on arrival (T0), hour after arrival (T1) and at transfer to the general ward/{ICU} (T2). The outcomes of interest were: hospital admission, {ICU} admission, length of stay and 30 day mortality.
Results
A total of 300 patients were assessed for eligibility. Complete data was able to be collected for 274 patients on arrival at the {ED}. {NEWS} was significantly correlated with patient outcomes, including 30 day mortality, hospital admission, and length of stay at all-time points.
Conclusion
The {NEWS} measured at different time points was a good predictor of patient outcomes and can be of additional value in the {ED} to longitudinally monitor patients throughout their stay in the {ED} and in the hospital.},
pages = {111--115},
journaltitle = {Resuscitation},
shortjournal = {Resuscitation},
author = {Alam, N. and Vegting, I. L. and Houben, E. and van Berkel, B. and Vaughan, L. and Kramer, M. H. H. and Nanayakkara, P. W. B.},
shorttitle = {Effectiveness of Early Warning Scores for Early Severity Assessment in Outpatient Emergency Care},
abstract = {Background and {objectivesPatient} assessment and possible deterioration prediction are a healthcare priority. Increasing demand for outpatient emergency care services requires the implementation of simple, quick, and effective systems of patient evaluation and stratification. The purpose of this review is to identify the most effective Early Warning Score ({EWS}) for the early detection of the risk of complications when screening emergency outpatients for a potentially serious condition.Materials and {methodsSystematic} review of the bibliography made in 2022. Scientific articles in Spanish and English were collected from the databases and search engines of Pubmed, Cochrane, and Dialnet, which were published between 2017 and 2021 about {EWSs} and their capacity to predict complications.{ResultsFor} analysis eleven articles were selected. Eight dealt with the application of different early warning scores in outpatient situations, concluding that all the scoring systems they studied were applicable. Three evaluated the predictive ability of various scoring systems and found no significant differences in their results. The eight articles evaluated the suitability of {NEWS}/{NEWS}2 to outpatient conditions and concluded it was the most suitable in pre-hospital emergency settings.{ConclusionsThe} early warning scores that were studied can be applied at the pre-hospital level, as they can predict patient mortality in the short term (24 or 48 h) and support clinical patient evaluation and medical decision making. Among them, {NEWS}2 is the most suitable for screening potentially deteriorating medical emergency outpatients.},
pages = {894906},
journaltitle = {Frontiers in public health},
shortjournal = {Front Public Health},
author = {Burgos-Esteban, Amaya and Gea-Caballero, Vicente and Marín-Maicas, Patricia and Santillán-García, Azucena and Cordón-Hurtado, María de Valvanera and Marqués-Sule, Elena and Giménez-Luzuriaga, Marta and Juárez-Vela, Raúl and Sanchez-Gonzalez, Juan Luis and García-Criado, Jorge and Santolalla-Arnedo, Iván},
urldate = {2023-04-27},
date = {2022-01-01},
pmid = {35910902},
pmcid = {PMC9330632},
keywords = {Emergency Care, Emergency Medical Service (Ems), Emergency Medicine, Medicine, Scale},
file = {Full Text PDF (Open access):/home/ulinja/Zotero/storage/NFFHLGDV/Burgos-Esteban et al. - 2022 - Effectiveness of Early Warning Scores for Early Se.pdf:application/pdf},
abstract = {Early diagnosis of critically ill patients depends on the attention and observation of medical staff about different variables, as vital signs, results of laboratory tests, among other. Seriously ill patients usually have changes in their vital signs before worsening. Monitoring these changes is important to anticipate the diagnosis in order to initiate patients’ care. Prognostic indexes play a fundamental role in this context since they allow to estimate the patients’ health status. Besides, the adoption of electronic health records improved the availability of data, which can be processed by machine learning techniques for information extraction to support clinical decisions. In this context, this work aims to create a computational model able to predict the deterioration of patients’ health status in such a way that it is possible to start the appropriate treatment as soon as possible. The model was developed based on Deep Learning technique, a Recurrent Neural Networks, the Long Short-Term Memory, for the prediction of patient’s vital signs and subsequent evaluation of the patient’s health status severity through Prognostic Indexes commonly used in the health area. Experiments showed that it is possible to predict vital signs with good precision (accuracy {\textgreater} 80\%) and, consequently, predict the Prognostic Indexes in advance to treat the patients before deterioration. Predicting the patient’s vital signs for the future and use them for the Prognostic Index’ calculation allows clinical times to predict future severe diagnoses that would not be possible applying the current patient’s vital signs (50\%–60\% of cases would not be identified).},
pages = {113905},
journaltitle = {Expert Systems with Applications},
shortjournal = {Expert Systems with Applications},
author = {da Silva, Denise Bandeira and Schmidt, Diogo and da Costa, Cristiano André and da Rosa Righi, Rodrigo and Eskofier, Björn},
journaltitle = {International Journal of Telemedicine and Applications},
author = {Pahlevanynejad, S. and Niakan Kalhori, S.R. and Katigari, M.R. and Eshpala, R.H.},
date = {2023},
file = {Full Text:/home/ulinja/Zotero/storage/MTKHYSAJ/Pahlevanynejad et al. - 2023 - Personalized Mobile Health for Elderly Home Care .pdf:application/pdf},
eventtitle = {{MOBIHEALTH} 2015 - 5th {EAI} International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare through Innovations in Mobile and Wireless Technologies},
author = {Anzanpour, A. and Rahmani, A.-M. and Liljeberg, P. and Tenhunen, H.},
date = {2015},
keywords = {Internet of Things, Body Area Network, {EarlyWarning} Score, Remote Patient Monitoring, Wearable electronics, Wireless Sensor Network},
file = {Full Text:/home/ulinja/Zotero/storage/37NIRLAE/Anzanpour et al. - 2015 - Internet of things enabled in-home health monitori.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/BSQHA7RC/display.html:text/html},
journaltitle = {Clinical and Experimental Emergency Medicine},
author = {Dagan, A. and Mechanic, O.J.},
date = {2020},
keywords = {Internet of Things, Fitness trackers, Global health, Monitoring, physiologic, Telemedicine},
file = {Full Text:/home/ulinja/Zotero/storage/2F69NQX4/Dagan and Mechanic - 2020 - Use of ultra-low cost fitness trackers as clinical.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/EV9AC9P6/display.html:text/html},
title = {{CuraBand}: Health Monitoring and Warning System},
isbn = {9789811551130},
doi = {10.1007/978-981-15-5113-0_86},
series = {Advances in Intelligent Systems and Computing},
shorttitle = {{CuraBand}},
abstract = {Phaltankar, {SopanTyagi}, {KirtiPrabhu}, {MeghnaJaguste}, {PranavSahu}, {ShubhamKalbande}, {DhananjayWe} have come to an era where the hospitals have increasing needs in terms of staff members. The ratio of patients to hospital staff has increased drastically over the years. One nurse cannot continuously monitor all the patients throughout the day. Hence, there is a constant desire for devices for continuous monitoring of patients that are just shifted outside the {ICU} and are still at grave risk. This paper deals with the design and development of a smart wristband—{CuraBand}, which is a compact vital parameter monitoring device. It continually monitors the temperature, heart rate, and blood oxygen saturation level and alerts the respective authorities in case of abnormalities in the readings through an android application, thus ensuring immediate action. This project aims to provide a provision for the doctors to monitor their patients from anywhere in the world.},
pages = {1017--1026},
booktitle = {International Conference on Innovative Computing and Communications},
publisher = {Springer},
author = {Phaltankar, Sopan and Tyagi, Kirti and Prabhu, Meghna and Jaguste, Pranav and Sahu, Shubham and Kalbande, Dhananjay},
editor = {Gupta, Deepak and Khanna, Ashish and Bhattacharyya, Siddhartha and Hassanien, Aboul Ella and Anand, Sameer and Jaiswal, Ajay},
date = {2021},
langid = {english},
keywords = {Cloud computing, Alert, Android application, Health care, Internet of things, {IoT}, Vital parameters, Wristband},
file = {Full Text PDF:/home/ulinja/Zotero/storage/H4IPCNUM/Phaltankar et al. - 2021 - CuraBand Health Monitoring and Warning System.pdf:application/pdf},
author = {Thippeswamy, V.S. and Shivakumaraswamy, P.M. and Chickaramanna, S.G. and Iyengar, V.M. and Das, A.P. and Sharma, A.},
date = {2021},
keywords = {Real-time monitoring, {ECG}, {ICU}, Internet of things, Heart rate, {SpO}2, Vital signs},
file = {Full Text:/home/ulinja/Zotero/storage/8XZ7QJYE/Thippeswamy et al. - 2021 - Prototype development of continuous remote monitor.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/B7RR7ZAW/display.html:text/html},
title = {{IoT} based Real Time Health Monitoring},
doi = {10.1109/ICIRCA48905.2020.9183194},
abstract = {Conventional sensor based diagnosis in medial field requires more number of sensors and human efforts if it is processed in a large scale. It is a difficult task due to the shortage of medical professionals and system setup. To overcome this issue an {IoT} based health care application is proposed in the research work. The proposed system consists of the web and mobile application based on continuous wireless monitoring of patients. The objective is paper is to implement a low-cost system and transmit the patient vital signs in emergency situations. Sensors are being used for measuring the patient vital signs by using the wireless network. The sensors data are collected and transmitted to the cloud for storage via Wi-Fi module connected with the controller. The data is processed in the cloud and feedback steps are taken on the analysed data which can be further analysed by a doctor remotely. Remote viewing reduces burden to doctors and provides the exact health status of patients. If the patient needs urgent attention then a message is sent to the doctor.},
eventtitle = {2020 Second International Conference on Inventive Research in Computing Applications ({ICIRCA})},
pages = {980--984},
booktitle = {2020 Second International Conference on Inventive Research in Computing Applications ({ICIRCA})},
author = {Yeri, Vani and Shubhangi, D.C.},
date = {2020-07},
keywords = {Cloud computing, Medical services, Monitoring, {IoT}, Arduino, Health, monitoring, patient, sensor, Temperature measurement, Temperature sensors, wireless, Wireless communication, Wireless sensor networks},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/I86F2Q3I/9183194.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/FS73U9GZ/Yeri and Shubhangi - 2020 - IoT based Real Time Health Monitoring.pdf:application/pdf},
journaltitle = {{IEEE} Transactions on Industrial Electronics},
author = {Lee, S. and Gandla, S. and Naqi, M. and Jung, U. and Youn, H. and Pyun, D. and Rhee, Y. and Kang, S. and Kwon, H.-J. and Kim, H. and Lee, M.G. and Kim, S.},
file = {Full Text:/home/ulinja/Zotero/storage/W38FIN5E/Lee et al. - 2020 - All-day mobile healthcare monitoring system based .pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/4GFD9BEF/display.html:text/html},
file = {Full Text:/home/ulinja/Zotero/storage/VXWDWLNI/Eisenkraft et al. - 2023 - Developing a real-time detection tool and an early.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/TJCUTVMT/display.html:text/html},
file = {Accepted Version:/home/ulinja/Zotero/storage/B4RXIEJI/Downey et al. - 2017 - Strengths and limitations of early warning scores.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/C4DPHSQ6/display.html:text/html},
abstract = {Background Patients admitted to the emergency care setting with {COVID}-19-infection can suffer from sudden clinical deterioration, but the extent of deviating vital signs in this group is still unclear. Wireless technology monitors patient vital signs continuously and might detect deviations earlier than intermittent measurements. The aim of this study was to determine frequency and duration of vital sign deviations using continuous monitoring compared to manual measurements. A secondary analysis was to compare deviations in patients admitted to {ICU} or having fatal outcome vs. those that were not. Methods Two wireless sensors continuously monitored ({CM}) respiratory rate ({RR}), heart rate ({HR}), and peripheral arterial oxygen saturation ({SpO}2). Frequency and duration of vital sign deviations were compared with point measurements performed by clinical staff according to regional guidelines, the National Early Warning Score ({NEWS}). Results {SpO}2 {\textless} 92\% for more than 60 min was detected in 92\% of the patients with {CM} vs. 40\% with {NEWS} (p {\textless} .00001). {RR} {\textgreater} 24 breaths per minute for more than 5 min were detected in 70\% with {CM} vs. 33\% using {NEWS} (p = .0001). {HR} ≥ 111 for more than 60 min was seen in 51\% with {CM} and 22\% with {NEWS} (p = .0002). Patients admitted to {ICU} or having fatal outcome had longer durations of {RR} {\textgreater} 24 brpm (p = .01), {RR} {\textgreater} 21 brpm (p = .01), {SpO}2 {\textless} 80\% (p = .01), and {SpO}2 {\textless} 85\% (p = .02) compared to patients that were not. Conclusion Episodes of desaturation and tachypnea in hospitalized patients with {COVID}-19 infection are common and often not detected by routine measurements.},
author = {Grønbæk, Katja Kjær and Rasmussen, Søren Møller and Langer, Natasha Hemicke and Vincentz, Mette and Oxbøll, Anne-Britt and Søgaard, Marlene and Awada, Hussein Nasser and Jensen, Tomas O. and Jensen, Magnus Thorsten and Sørensen, Helge B. D. and Aasvang, Eske Kvanner and Meyhoff, Christian Sylvest},
file = {Full Text PDF:/home/ulinja/Zotero/storage/P9XWRWXW/Grønbæk et al. - 2023 - Continuous monitoring is superior to manual measur.pdf:application/pdf},
abstract = {Patients infected with {SARS}-{CoV}-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild {COVID}-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 32years; male: 52.9\%) with mild {COVID}-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 ({NEWS}2) were 0.59±0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r=0.96, p{\textless}0.0001) and oxygen saturation (r=0.87, p{\textless}0.0001). A machine learning-derived index reflecting overall health status, Biovitals Index ({BI}), was generated by autonomous analysis of physiology parameters, symptoms, and other medical data. Daily {BI} was linearly associated with respiratory tract viral load (p{\textless}0.0001) and {NEWS}2 (r=0.75, p{\textless}0.001). {BI} was superior to {NEWS}2 in predicting clinical worsening events (sensitivity 94.1\% and specificity 88.9\%) and prolonged hospitalization (sensitivity 66.7\% and specificity 72.7\%). Wearable biosensors coupled with machine learning-derived health index allowed automated detection of clinical deterioration.},
pages = {4388},
number = {1},
journaltitle = {Scientific Reports},
shortjournal = {Sci Rep},
author = {Un, Ka-Chun and Wong, Chun-Ka and Lau, Yuk-Ming and Lee, Jeffrey Chun-Yin and Tam, Frankie Chor-Cheung and Lai, Wing-Hon and Lau, Yee-Man and Chen, Hao and Wibowo, Sandi and Zhang, Xiaozhu and Yan, Minghao and Wu, Esther and Chan, Soon-Chee and Lee, Sze-Ming and Chow, Augustine and Tong, Raymond Cheuk-Fung and Majmudar, Maulik D. and Rajput, Kuldeep Singh and Hung, Ivan Fan-Ngai and Siu, Chung-Wah},
abstract = {Introduction: Monitoring a patient’s vital signs forms a basic component of care, enabling the identification of deteriorating patients and increasing the likelihood of improving patient outcomes. Several paper-based track and trigger warning scores have been developed to allow clinical evaluation of a patient and guidance on escalation protocols and frequency of monitoring. However, evidence suggests that patient deterioration on hospital wards is still missed, and that patients are still falling through the safety net. Wearable sensor technology is currently undergoing huge growth, and the development of new light-weight wireless wearable sensors has enabled multiple vital signs monitoring of ward patients continuously and in real time.Areas covered: In this paper, we aim to closely examine the benefits of wearable monitoring applications that measure multiple vital signs; in the context of improving healthcare and delivery. A review of the literature was performed.Expert commentary: Findings suggest that several sensor designs are available with the potential to improve patient safety for both hospital patients and those at home. Larger clinical trials are required to ensure both diagnostic accuracy and usability.},
pages = {145--154},
number = {2},
journaltitle = {Expert Review of Medical Devices},
author = {Joshi, Meera and Ashrafian, Hutan and Aufegger, Lisa and Khan, Sadia and Arora, Sonal and Cooke, Graham and Darzi, Ara},
shorttitle = {Patient attitudes towards remote continuous vital signs monitoring on general surgery wards},
abstract = {Background
Vital signs monitoring is used to identify deteriorating patients in hospital. The most common tool for vital signs monitoring is an early warning score, although emerging technologies allow for remote, continuous patient monitoring. A number of reviews have examined the impact of continuous monitoring on patient outcomes, but little is known about the patient experience. This study aims to discover what patients think of monitoring in hospital, with a particular emphasis on intermittent early warning scores versus remote continuous monitoring, in order to inform future implementations of continuous monitoring technology.
Methods
Semi-structured interviews were undertaken with 12 surgical inpatients as part of a study testing a remote continuous monitoring device. All patients were monitored with both an early warning score and the new device. Interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis.
Findings
Patients can see the value in remote, continuous monitoring, particularly overnight. However, patients appreciate the face-to-face aspect of early warning score monitoring as it allows for reassurance, social interaction, and gives them further opportunity to ask questions about their medical care.
Conclusion
Early warning score systems are widely used to facilitate detection of the deteriorating patient. Continuous monitoring technologies may provide added reassurance. However, patients value personal contact with their healthcare professionals and remote monitoring should not replace this. We suggest that remote monitoring is best introduced in a phased manner, and initially as an adjunct to usual care, with careful consideration of the patient experience throughout.},
pages = {52--56},
journaltitle = {International Journal of Medical Informatics},
shortjournal = {International Journal of Medical Informatics},
author = {Downey, C. L. and Brown, J. M. and Jayne, D. G. and Randell, R.},
abstract = {Continuous vital signs monitoring in post-surgical ward patients may support early detection of clinical deterioration, but novel alarm approaches are required to ensure timely notification of abnormalities and prevent alarm-fatigue. The current study explored the performance of classical and various adaptive threshold-based alarm strategies to warn for vital sign abnormalities observed during development of an adverse event. A classical threshold-based alarm strategy used for continuous vital signs monitoring in surgical ward patients was evaluated retrospectively. Next, (combinations of) six methods to adapt alarm thresholds to personal or situational factors were simulated in the same dataset. Alarm performance was assessed using the overall alarm rate and sensitivity to detect adverse events. Using a wireless patch-based monitoring system, 3999 h of vital signs data was obtained in 39 patients. The clinically used classical alarm system produced 0.49 alarms/patient/day, and alarms were generated for 11 out of 18 observed adverse events. Each of the tested adaptive strategies either increased sensitivity to detect adverse events or reduced overall alarm rate. Combining specific strategies improved overall performance most and resulted in earlier presentation of alarms in case of adverse events. Strategies that adapt vital sign alarm thresholds to personal or situational factors may improve early detection of adverse events or reduce alarm rates as compared to classical alarm strategies. Accordingly, further investigation of the potential of adaptive alarms for continuous vital signs monitoring in ward patients is warranted.},
pages = {407--417},
number = {2},
journaltitle = {Journal of Clinical Monitoring and Computing},
shortjournal = {J Clin Monit Comput},
author = {van Rossum, Mathilde C. and Vlaskamp, Lyan B. and Posthuma, Linda M. and Visscher, Maarten J. and Breteler, Martine J. M. and Hermens, Hermie J. and Kalkman, Cor J. and Preckel, Benedikt},
abstract = {Background: Complications after surgical procedures are common and can lead to a prolonged hospital stay, increased rates of postoperative hospital readmission, and increased mortality. Monitoring vital signs is an effective way to identify patients who are experiencing a deterioration in health. {SensiumVitals} is wireless system that includes a lightweight, digital patch that monitors vital signs at two minute intervals, and has shown promise in the early identification of patients at high risk of deterioration.Objective: To evaluate the cost-utility of continuous monitoring of vital signs with {SensiumVitals} in addition to intermittent monitoring compared to the usual care of patients admitted to surgical wards.Methods: A de novo decision analytic model, based on current treatment pathways, was developed to estimate the costs and outcomes. Results from randomised clinical trials and national standard sources were used to inform the model. Costs were estimated from the {NHS} and {PSS} perspective. Deterministic and probabilistic sensitivity analyses ({PSA}) were conducted to explore uncertainty surrounding input parameters.Results: Over a 30-day time horizon, intermittent monitoring in addition to continuous monitoring of vital signs with {SensiumVitals} was less costly than intermittent vital signs monitoring alone. The total cost per patient was £6,329 versus £5,863 for the comparator and intervention groups respectively and the total effectiveness per patient was 0.057 {QALYs} in each group. Results from the {PSA} showed that use of {SensiumVitals} in addition to intermittent monitoring has 73\% probability of being cost-effective at a £20,000 willingness-to-pay threshold and 73\% probability of being cost-saving compared to the comparator. Cost savings were driven by reduced costs of hospital readmissions and length of stays in hospital.Conclusions: Use of {SensiumVitals} as a postoperative intervention for patients on surgical wards is a cost-saving and cost-effective strategy, yielding improvements in recovery with decreased health resource use.Key Points for Decision {MakersSensiumVitals} has the potential to reduce the length of postoperative hospital stay, readmission rates, and associated costs in postoperative patients.In this study, {SensiumVitals} has been found to be a cost-saving (dominant) and cost-effective (dominant) intervention for monitoring the vital signs of surgical patients postoperatively.},
pages = {728--736},
number = {7},
journaltitle = {Journal of Medical Economics},
author = {Javanbakht, Mehdi and Mashayekhi, Atefeh and Trevor, Miranda and Rezaei Hemami, Mohsen and L. Downey, Candice and Branagan-Harris, Michael and Atkinson, Jowan},
file = {Full Text PDF:/home/ulinja/Zotero/storage/ZZ7Q5R9K/Javanbakht et al. - 2020 - Cost utility analysis of continuous and intermitte.pdf:application/pdf},
shorttitle = {Association between clinically abnormal observations and subsequent in-hospital mortality},
abstract = {Background: Patients with unexpected in-hospital cardiac arrest often have an abnormal clinical observation prior to the arrest. Previous studies have suggested that a medical emergency team responding to such patients may decrease in-hospital mortality from cardiac arrest, but the association between any abnormal clinical observation and subsequent increased mortality has not been studied prospectively. The aim of this study was to determine the predictive value of selected abnormal clinical observations in a ward population for subsequent in-hospital mortality. Design and setting: Prospective data collection in five general hospital ward areas at Dandenong Hospital, Victoria, Australia. Interventions: None. Results: During the study period, 6303 patients were admitted to the study areas. Of those, 564 (8.9\%) experienced 1598 pre-determined clinically abnormal events and 146 of these patients (26\%) died. The two commonest abnormal clinical events were arterial oxygen desaturation (51\% of all events), and hypotension (17.3\% of all events). Using a multiple linear logistic regression model, there were six clinical observations which were significant predictors of mortality. These were: a decrease in Glasgow Coma Score by two points, onset of coma, hypotension ({\textless}90mmHg), respiratory rate {\textless}6min−1, oxygen saturation {\textless}90\%, and bradycardia {\textgreater}30min−1. The presence of any one of the six events was associated with a 6.8-fold (95\% {CI}: 2.7–17.1) increase in the risk of mortality. Conclusions: Six abnormal clinical observations are associated with a high risk of mortality for in-hospital patients. These observations should be included as criteria for the early identification of patients at higher risk of unexpected in-hospital cardiac arrest.
Sumàrio
Contexto: Os doentes vı́timas de paragem cardı́aca intra-hospitalar inesperada têm frequentemente uma observação clı́nica anormal antes da paragem. Estudos prévios sugerem que se esses doentes forem socorridos por uma equipa médica de emergência pode-se diminuir a mortalidade intra-hospitalar por paragem cardı́aca, mas a associação entre alterações clı́nicas e aumento da mortalidade subsequente não foi estudada de forma prospectiva. O objectivo deste estudo foi determinar o valor preditivo, para mortalidade intra-hospitalar de alterações clı́nicas, seleccionadas, numa população de enfermaria. Desenho: Recolha prospectiva de dados em cinco áreas de enfermaria geral hospitalar no Hospital Dandenong, Victoria, Austrália. Intervenções: Nenhuma. Resultados: Durante o perı́odo de estudo, foram admitidos 6303 doentes nas áreas de estudo. Em 564 (8.9\%) ocorreram 1598 alterações clı́nicas de um grupo pré-determinado e 146 (26\%) morreram. As duas alterações clı́nicas mais frequentes foram a dessaturação de oxigénio arterial (51\% de todos os eventos), e a hipotensão (17.3\% de todos os eventos). Utilizando um modelo de regressão logı́stica linear múltipla, houve seis observações clı́nicas que significativamente preditivas de mortalidade: Deterioração do Score da Escala de Coma de Glasgow em 2 pontos, inı́cio de coma, hipotensão ({\textless}90mmHg), frequência respiratória {\textless}6min, saturação de oxigénio {\textless}90\%, e bradicardia {\textgreater}30min. A presença de qualquer um destes seis acontecimentos associou-se ao aumento de 6.8 vezes (95\% {CI}: 2.7–17.1) no risco de morte. Conclusões: Identificaram-se seis alterações clı́nicas associadas a aumento de risco de morte em doentes hospitalizados. Estas observações devem ser incluı́das como critérios para a identificação precoce dos doentes com risco mais elevado de paragem cardı́aca intra-hospitalar inesperada.
Resumen
Antecedentes: Los pacientes con paro cardı́aco no esperado intrahospitalario tienen frecuentemente hallazgos clı́nicos anormales previos al paro. Estudios previos sugieren que el equipo de emergencias médicas que responde a tales pacientes podrı́a disminuir la mortalidad intrahospitalaria, pero la asociación entre los hallazgos clı́nicos anormales y mortalidad aumentada subsiguiente no ha sido estudiada prospectivamente. El objetivo de este estudio fue determinar el valor de determinados hallazgos clı́nicos anormales para predecir mortalidad intrahospitalaria. Diseño y Ambiente: Recolección prospectiva de datos en cinco áreas de salas generales en el Hospital Dandenong, en Victoria, Australia. Intervenciones: ninguna. Resultados: Durante el perı́odo de estudio, 6303 pacientes fueron admitidos en las áreas del estudio. De aquellos, 564 (8.9\%) experimentaron 1598 eventos clı́nicamente anormales y 164 de estos pacientes (26\%) murieron. Los dos eventos clı́nicos anormales mas comunes fueron la des saturación (51\% de las alertas), y la hipotensión (17.3\% de los eventos). Se analizó usando un modelo de regresión logı́stica linear múltiple, y se encontraron seis hallazgos clı́nicos que eran predictores significativos de mortalidad. Estos fueron: una disminución en dos puntos en la escala de coma de Glasgow, instalación de coma, hipotensión ({\textless}90mmHg), frecuencia respiratoria {\textless} 6min−1, saturación de oxı́geno {\textless}90\%, y bradicardia {\textgreater}30min−1. La presencia de cualquiera de estos 6 eventos se asoció con un aumento en 6.8 veces del riesgo de mortalidad (95\% {CI}, 2.7–17.1). Conclusiones: Seis hallazgos clı́nicos anormales están asociadas con mayor riesgo de mortalidad intrahospitalaria de pacientes. Estos hallazgos clı́nicos deberı́an incluirse como criterios para la identificación temprana de pacientes en mayor riesgo de paro cardı́aco intrahospitalario inesperado.},
pages = {137--141},
number = {2},
journaltitle = {Resuscitation},
shortjournal = {Resuscitation},
author = {Buist, Michael and Bernard, Stephen and Nguyen, Tuan V and Moore, Gaye and Anderson, Jeremy},
shorttitle = {The value of vital sign trends in predicting and monitoring clinical deterioration},
abstract = {Background Vital signs, i.e. respiratory rate, oxygen saturation, pulse, blood pressure and temperature, are regarded as an essential part of monitoring hospitalized patients. Changes in vital signs prior to clinical deterioration are well documented and early detection of preventable outcomes is key to timely intervention. Despite their role in clinical practice, how to best monitor and interpret them is still unclear. Objective To evaluate the ability of vital sign trends to predict clinical deterioration in patients hospitalized with acute illness. Data Sources {PubMed}, Embase, Cochrane Library and {CINAHL} were searched in December 2017. Study Selection Studies examining intermittently monitored vital sign trends in acutely ill adult patients on hospital wards and in emergency departments. Outcomes representing clinical deterioration were of interest. Data Extraction Performed separately by two authors using a preformed extraction sheet. Results Of 7,366 references screened, only two were eligible for inclusion. Both were retrospective cohort studies without controls. One examined the accuracy of different vital sign trend models using discrete-time survival analysis in 269,999 admissions. One included 44,531 medical admissions examining trend in Vitalpac Early Warning Score weighted vital signs. They stated that vital sign trends increased detection of clinical deterioration. Critical appraisal was performed using evaluation tools. The studies had moderate risk of bias, and a low certainty of evidence. Additionally, four studies examining trends in early warning scores, otherwise eligible for inclusion, were evaluated. Conclusions This review illustrates a lack of research in intermittently monitored vital sign trends. The included studies, although heterogeneous and imprecise, indicates an added value of trend analysis. This highlights the need for well-controlled trials to thoroughly assess the research question.},
pages = {e0210875},
number = {1},
journaltitle = {{PLOS} {ONE}},
shortjournal = {{PLOS} {ONE}},
author = {Brekke, Idar Johan and Puntervoll, Lars Håland and Pedersen, Peter Bank and Kellett, John and Brabrand, Mikkel},
file = {Full Text PDF:/home/ulinja/Zotero/storage/5VV8R3MF/Brekke et al. - 2019 - The value of vital sign trends in predicting and m.pdf:application/pdf},
title = {The effectiveness of a national early warning score as a triage tool for activating a rapid response system in an outpatient setting: A retrospective cohort study},
shorttitle = {The effectiveness of a national early warning score as a triage tool for activating a rapid response system in an outpatient setting},
abstract = {Rapid response system ({RRS}) efficacy and national early warning score ({NEWS}) performances have largely been reported in inpatient settings, with few such reports undertaken in outpatient settings.
This study aimed to investigate {NEWS} validity in predicting poor clinical outcomes among outpatients who had activated the {RRS} using single-parameter criteria.
A single-center retrospective cohort study
From April 1, 2014 to November 30, 2017 in an urban 350-bed referral hospital in Japan
We collected patient characteristics such as activation triggers, interventions, arrival times, dispositions, final diagnoses, and patient outcomes. Poor clinical outcomes were defined as unplanned intensive care unit transfers or deaths within 24 hours. Correlations between the {NEWS} and clinical outcomes at the time of deterioration and disposition were analyzed.
Among 31 outpatients, the {NEWS} value decreased significantly after a medical emergency team intervention (median, 8 vs 4, P {\textless} .001). The difference in the {NEWS} at the time of deterioration and at disposition was significantly less in patients with poor clinical outcomes (median 3 vs 1.5, P = .03). The area under the curve ({AUC}) for the {NEWS} high-risk patient group at the time of deterioration for predicting hospital admission was 0.85 (95\% confidence interval [{CI}], 0.67–1.0), while the {AUC} for the {NEWS} high-risk patient group at disposition for predicting poor clinical outcomes was 0.83 (95\% {CI}, 0.62–1.0).
The difference between the {NEWS} at the time of deterioration and at disposition might usefully predict admissions and poor clinical outcomes in {RRS} outpatient settings.},
pages = {e18475},
number = {52},
journaltitle = {Medicine},
author = {Ehara, Jun and Hiraoka, Eiji and Hsu, Hsiang-Chin and Yamada, Toru and Homma, Yosuke and Fujitani, Shigeki},
urldate = {2023-04-26},
date = {2019-12},
langid = {american},
file = {Full Text:/home/ulinja/Zotero/storage/C5GPM6LE/Ehara et al. - 2019 - The effectiveness of a national early warning scor.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/PCS2IG9C/The_effectiveness_of_a_national_early_warning.30.html:text/html},
title = {Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology},
volume = {20},
rights = {http://creativecommons.org/licenses/by/3.0/},
abstract = {In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients’ vital signs using wearable technology as a basis for real-time early warning scores ({EWS}) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the {EWS} for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of {kNN}-{LS}-{SVM} for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of {EWS} in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of {LSTM}. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5\% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of {EWS} in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate {EWS} computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.},
pages = {6593},
number = {22},
journaltitle = {Sensors},
author = {Youssef Ali Amer, Ahmed and Wouters, Femke and Vranken, Julie and de Korte-de Boer, Dianne and Smit-Fun, Valérie and Duflot, Patrick and Beaupain, Marie-Hélène and Vandervoort, Pieter and Luca, Stijn and Aerts, Jean-Marie and Vanrumste, Bart},
urldate = {2023-04-26},
date = {2020-01},
langid = {english},
note = {Number: 22
Publisher: Multidisciplinary Digital Publishing Institute},
file = {Full Text PDF:/home/ulinja/Zotero/storage/FAEVF9FC/Youssef Ali Amer et al. - 2020 - Vital Signs Prediction and Early Warning Score Cal.pdf:application/pdf},
abstract = {Make full use of modern mobile communication ({GPRS}) and the Internet (Ineternet) transmission technology on the basis of the traditional custodians of the wireless transmission of real-time data.},
title = {A {QRS} Detection and R Point Recognition Method for Wearable Single-Lead {ECG} Devices},
volume = {17},
rights = {http://creativecommons.org/licenses/by/3.0/},
issn = {1424-8220},
url = {https://www.mdpi.com/1424-8220/17/9/1969},
doi = {10.3390/s17091969},
abstract = {In the new-generation wearable Electrocardiogram ({ECG}) system, signal processing with low power consumption is required to transmit data when detecting dangerous rhythms and to record signals when detecting abnormal rhythms. The {QRS} complex is a combination of three of the graphic deflection seen on a typical {ECG}. This study proposes a real-time {QRS} detection and R point recognition method with low computational complexity while maintaining a high accuracy. The enhancement of {QRS} segments and restraining of P and T waves are carried out by the proposed {ECG} signal transformation, which also leads to the elimination of baseline wandering. In this study, the {QRS} fiducial point is determined based on the detected crests and troughs of the transformed signal. Subsequently, the R point can be recognized based on four {QRS} waveform templates and preliminary heart rhythm classification can be also achieved at the same time. The performance of the proposed approach is demonstrated using the benchmark of the {MIT}-{BIH} Arrhythmia Database, where the {QRS} detected sensitivity (Se) and positive prediction (+P) are 99.82\% and 99.81\%, respectively. The result reveals the approach’s advantage of low computational complexity, as well as the feasibility of the real-time application on a mobile phone and an embedded system.},
file = {Full Text PDF:/home/ulinja/Zotero/storage/TAYBJYZT/Chen and Chuang - 2017 - A QRS Detection and R Point Recognition Method for.pdf:application/pdf},
title = {Features of electronic Early Warning systems which impact clinical decision making},
doi = {10.1109/CBMS.2012.6266394},
abstract = {Paper-based Modified Early Warning Scorecards ({MEWS}) have been developed to help nursing staff detect hospital in-patient deterioration at an early stage. {MEWS} is based on patient vital signs where these values are transformed into a {MEWS} score. An electronic Modified Early Warning Scorecard ({eMEWS}) prototype has been designed and developed to fulfill the role of a computerized Clinical Decision Support System ({CDSS}) and to assist healthcare professionals in their decision making activities. A review of the existing electronic Early Warning Scorecards ({eEWS}) revealed they lack certain features that assist in capturing a holistic view of the patient health status for example color codes and vital sign trends. The proposed {eMEWS} prototype employs these features with the aim of assisting healthcare professionals to obtain a clear understanding of the patient status. A survey was conducted to evaluate the impact of paper-based {MEWS} and {eMEWS} as part of the decision making process. The advantages and disadvantages of {eMEWS} over the paper-based {MEWS} are presented.},
eventtitle = {2012 25th {IEEE} International Symposium on Computer-Based Medical Systems ({CBMS})},
pages = {1--4},
booktitle = {2012 25th {IEEE} International Symposium on Computer-Based Medical Systems ({CBMS})},
author = {Zarabzadeh, Atieh and O'Connell, Mervyn and O'Donoghue, John and O'Kane, Tom and Woodworth, Simon and Gallagher, Joe and O'Connor, Siobhán and Adam, Frederic},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/URGX63FD/6266394.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/L2TKQQ3Y/Zarabzadeh et al. - 2012 - Features of electronic Early Warning systems which.pdf:application/pdf},
abstract = {The most burning issues worldwide at present are the availability, accessibility, and affordability of the equitable healthcare services for all. It is getting more severe for developing countries due to increasing population and chronic diseases. The emerging technological interventions in the field of Internet of Things({IoT})-based healthcare systems are a promising solution to meet the general public's healthcare needs. Therefore, an {IoT}-enabled vital sign monitoring system has been presented in this paper. The presented system can monitor various vital signs in real-time and store the recorded trends locally. The system can also send the data into cloud for further analysis. Abnormality detection with alert notification and automatic calculation of early warning score has been implemented. An Android application is developed to store the vital signs records on a personal server to avoid the burden and maintenance cost of the central medical server. The presented system is straightforward, compact, portable and easy to operate through personal service application. Also, the presented system is compared with the most recent work available in the field.},
pages = {129--156},
number = {1},
journaltitle = {Wireless Personal Communications},
shortjournal = {Wireless Pers Commun},
author = {Sahu, Manju Lata and Atulkar, Mithilesh and Ahirwal, Mitul Kumar and Ahamad, Afsar},
urldate = {2023-04-26},
date = {2022-01-01},
langid = {english},
keywords = {Real-time monitoring, Healthcare, Abnormality detection, Alert notification, Internet of thing, Mobile communication, Personal service application, Vital sign monitoring},
file = {Full Text PDF:/home/ulinja/Zotero/storage/XTBR4NVR/Sahu et al. - 2022 - Vital Sign Monitoring System for Healthcare Throug.pdf:application/pdf},
abstract = {The availability, accessibility, and affordability of good healthcare services to remote, rural, and developing parts of the world is a major challenge. To resolve this dynamically growing issue of global importance, there is a need to devise an integrated and intelligent solution for the delivery of health monitoring services along with abnormality detection and alert notification. In this work, a remote patient monitoring system ({RPMS}) has been presented. Internet of things ({IoT}) and integrated cloud computing technologies are used for the implementation. The system can continuously measure different physiological parameters with the appropriate degree of accuracy required by medical standards. A mobile application has been developed for Android devices, which acts as a gateway between {RPMS} and the Cloud. The developed mobile application offers visualization and storage of physiological parameters locally as well as in Cloud along with real-time data transmission for remote monitoring and further analysis. In case of an abnormal event and emergency, the system can generate an alert notification to the local user and remote supervisor. The {RPMS} has been implemented and validated on the state-of-the-art patient monitoring system. A series of tests have been carried out to validate the system’s effectiveness and reliability for measuring different physiological parameters and its remote monitoring in real-time. In addition to this performance analysis of the cloud-based system for real-time data transmission has also been carried out.},
pages = {1894--1909},
number = {5},
journaltitle = {Mobile Networks and Applications},
shortjournal = {Mobile Netw Appl},
author = {Sahu, Manju Lata and Atulkar, Mithilesh and Ahirwal, Mitul Kumar and Ahamad, Afsar},
keywords = {Remote patient monitoring, Abnormality detection, Internet of thing, Alert Notification, Mobile Communication},
file = {Full Text PDF:/home/ulinja/Zotero/storage/BUVVMQQ9/Sahu et al. - 2022 - Cloud-Based Remote Patient Monitoring System with .pdf:application/pdf},
shorttitle = {Prediction of in-hospital mortality and length of stay using an early warning scoring system},
abstract = {{\textless}p{\textgreater}This aim of this study was to assess the impact of the introduction of a standardised early warning scoring system ({SEWS}) on physiological observations and patient outcomes in unselected acute admissions at point of entry to care. A sequential clinical audit was performed on 848 patients admitted to a combined medical and surgical assessment unit during two separate 11-day periods. Physiological parameters (respiratory rate, oxygen saturation, temperature, blood pressure, heart rate, and conscious level), in-hospital mortality, length of stay, transfer to critical care and staff satisfaction were documented. Documentation of these physiological parameters improved (P\<0.001–0.005) with the exception of oxygen saturation (P=0.069). The admission early warning score correlated both with in-hospital mortality (P\<0.001) and length of stay (P=0.001). Following the introduction of the scoring system, inpatient mortality decreased (P=0.046). Staff responding to a questionnaire indicated that the scoring system increased awareness of illness severity (80\%) and prompted earlier interventions (60\%). A standardised early warning scoring system improves documentation of physiological parameters, correlates with in-hospital mortality, and helps predict length of stay.{\textless}/p{\textgreater}},
pages = {281--284},
number = {3},
journaltitle = {Clinical Medicine},
author = {Paterson, R. and {MacLeod}, D. C. and Thetford, D. and Beattie, A. and Graham, C. and Lam, S. and Bell, D.},
urldate = {2023-04-26},
date = {2006-05-01},
langid = {english},
pmid = {16826863},
note = {Publisher: Royal College of Physicians
Section: Original Papers},
file = {Full Text PDF:/home/ulinja/Zotero/storage/TNBWXWKF/Paterson et al. - 2006 - Prediction of in-hospital mortality and length of .pdf:application/pdf},
shorttitle = {Can early warning scores identify deteriorating patients in pre-hospital settings?},
abstract = {Objective
To evaluate the effectiveness and predictive accuracy of early warning scores ({EWS}) to predict deteriorating patients in pre-hospital settings.
Methods
Systematic review. Seven databases searched to August 2017. Study quality was assessed using {QUADAS}-2. A narrative synthesis is presented.
Eligibility
Studies that evaluated {EWS} predictive accuracy or that compared outcomes in populations that did or did not use {EWS}, in any pre-hospital setting were eligible for inclusion. {EWS} were included if they aggregated three or more physiological parameters.
Seventeen studies (157,878 participants) of predictive accuracy were included (16 in ambulance service and 1 in nursing home). {AUCs} ranged from 0.50 ({CI} not reported) to 0.89 (95\%{CI} 0.82, 0.96). {AUCs} were generally higher ({\textgreater}0.80) for prediction of mortality within short time frames or for combination outcomes that included mortality and {ICU} admission. Few patients with low scores died at any time point. Patients with high scores were at risk of deterioration. Results were less clear for intermediate thresholds (≥4 or 5). Five studies were judged at low or unclear risk of bias, all others were judged at high risk of bias.
Conclusions
Very low and high {EWS} are able to discriminate between patients who are not likely and those who are likely to deteriorate in the pre-hospital setting. No study compared outcomes pre- and post-implementation of {EWS} so there is no evidence on whether patient outcomes differ between pre-hospital settings that do and do not use {EWS}. Further studies are required to address this question and to evaluate {EWS} in pre-hospital settings.},
keywords = {Early warning score, Deteriorating patients, Critical care, Pre hospital setting, Track and trigger system},
file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/NKPJTMTR/Patel et al. - 2018 - Can early warning scores identify deteriorating pa.pdf:application/pdf;ScienceDirect Snapshot:/home/ulinja/Zotero/storage/P6WFVY87/S0300957218308190.html:text/html},
abstract = {Due to the {COVID}-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person contacts, enabling early-detection of severe cases, and remotely assessing patients’ status. Internet of Things ({IoT}) technologies have been used for monitoring patients’ health with wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining {IoT}-related technologies with early-warning scores ({EWS}) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the {NEWS}-2 has been showing remarkable results in detecting the health deterioration of {COVID}-19 patients. Although the literature presents several approaches for remote monitoring, none of these studies proposes a customized, complete, and integrated architecture that uses an effective early-detection mechanism for {COVID}-19 and that is flexible enough to be used in hospital wards and at home. Therefore, this article's objective is to present a comprehensive {IoT}-based conceptual architecture that addresses the key requirements of scalability, interoperability, network dynamics, context discovery, reliability, and privacy in the context of remote health monitoring of {COVID}-19 patients in hospitals and at home. Since remote monitoring of patients at home (essential during a pandemic) can engender trust issues regarding secure and ethical data collection, a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with {EWS}.},
pages = {100399},
journaltitle = {Internet of Things},
shortjournal = {Internet of Things},
author = {Paganelli, Antonio Iyda and Velmovitsky, Pedro Elkind and Miranda, Pedro and Branco, Adriano and Alencar, Paulo and Cowan, Donald and Endler, Markus and Morita, Plinio Pelegrini},
file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/GYESA337/Paganelli et al. - 2022 - A conceptual IoT-based early-warning architecture .pdf:application/pdf;ScienceDirect Snapshot:/home/ulinja/Zotero/storage/VNA4PMAC/S2542660521000433.html:text/html},
title = {{IoT} based Smart Healthcare Monitoring Systems: A Review},
doi = {10.1109/ISPCC53510.2021.9609393},
shorttitle = {{IoT} based Smart Healthcare Monitoring Systems},
abstract = {{IoT} devices are becoming very useful in today’s world. In today’s era {IoT} devices are increasing and it has a huge impact in healthcare. It can provide early detection of health problems and can reduce the cost of medical care. The healthcare monitoring system is required for a patient who needs to be monitored 24 X 7. The {IoT} based health monitoring system can monitor the vital health parameters of a person at all times. It can help patients in the case of emergency by providing immediate health consultation from the doctor available at a distant location. Further, {IoT} based smart systems enable remote monitoring of the patient by the guardian/ family member which is considered as one of the major advantages to save the precious human life. This paper provides an overview of various {IoT} based health monitoring systems. The comparison of various healthcare devices has also been presented by taking into consideration the important healthcare parameters.},
eventtitle = {2021 6th International Conference on Signal Processing, Computing and Control ({ISPCC})},
pages = {465--469},
booktitle = {2021 6th International Conference on Signal Processing, Computing and Control ({ISPCC})},
author = {Tiwari, Divyanshu and Prasad, Devendra and Guleria, Kalpna and Ghosh, Pinaki},
date = {2021-10},
note = {{ISSN}: 2643-8615},
keywords = {Biomedical monitoring, Medical services, Monitoring, {IoT}, Remote monitoring, Costs, Health monitoring, healthcare, heart monitoring devices, medical services, Signal processing},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/GTHWZ2L3/9609393.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/NANQYIU6/Tiwari et al. - 2021 - IoT based Smart Healthcare Monitoring Systems A R.pdf:application/pdf},
title = {Wireless sensor network-based smart room system for healthcare monitoring},
doi = {10.1109/ROBIO.2011.6181597},
abstract = {In this paper, the utilization of Zigbee as wireless sensor network ({WSN}) for medical application is demonstrated. The combination of various topologies is used to configure wireless sensors network to achieve high efficiency network architecture in medicine. The network consists of center coordinator, routers and sensor nodes. Mesh network is used for the connection between coordinator and router for range expansion. A performance of the proposed modality is tested in the normal situation. Besides, the architecture of the smart room systems is also proposed for healthcare monitoring. Physiological data and signal are transmitted using Xbee which is a wireless device operated in unlicensed radio frequency bands.},
eventtitle = {2011 {IEEE} International Conference on Robotics and Biomimetics},
pages = {2073--2076},
booktitle = {2011 {IEEE} International Conference on Robotics and Biomimetics},
author = {Arnil, Jetsada and Punsawad, Yunyong and Wongsawat, Yodchanan},
date = {2011-12},
keywords = {Medical services, Monitoring, Temperature measurement, Temperature sensors, Wireless communication, Wireless sensor networks, Zigbee},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/U4YYGJI4/6181597.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/UV6QAL2S/Arnil et al. - 2011 - Wireless sensor network-based smart room system fo.pdf:application/pdf},
}
@inproceedings{jagadish_remote_2018,
location = {Singapore},
title = {Remote Continuous Health Monitoring System for Patients},
isbn = {978-981-10-8603-8},
doi = {10.1007/978-981-10-8603-8_11},
series = {Communications in Computer and Information Science},
abstract = {Recent advances in Internet of Things ({IoT}) leads to the development of several applications. Some of these applications are related to smart healthcare monitoring though there are several applications for healthcare and monitoring it does not fully address the ability to satisfy the patient’s needs, doctor convenience and the automation of system to act according to the status of the patient. In this paper, we implemented an algorithm for (a) the continuous health monitoring and frequent update to the hospital server which notifies the doctor. It also supports the mobility of patient, (b) acts according to the critical levels of patient which is safe, intermediate or emergency through cloud processing, (c) ability of the doctor to view enormous sensor record in comprehend form using the visualization and analysis and (d) Drug management system to deliver drugs to the patient on time and without the presence of the nurse using stored data in the server. Raspberry pi 2 is used to transmit the bio-sensed data of patient directly to the cloud by using the internet of smart phone or {PC} where based on the critical level sends a notification to the doctor through hospital server, caretaker and/or Ambulance service.},
pages = {129--138},
booktitle = {Data Science Analytics and Applications},
publisher = {Springer},
author = {Jagadish, D. and Priya, N. and Suganya, R.},
title = {A Hospital Healthcare Monitoring System Using Internet of Things Technologies},
doi = {10.1109/SEEDA-CECNSM53056.2021.9566252},
abstract = {In any hospital health care monitoring system, it is necessary to constantly monitor the patient's physiological parameters. Previous studies report that important parameters of any patient that have to be monitored in hospital are heart rate, respiratory rate, oxygen saturation, temperature, change in systolic blood pressure, motion, posture and its location. This work presents a monitoring system that has the capability to monitor in real-time the physiological parameters of the patient using a comfortable wearable device. A Wireless Sensor Network in collaboration with multiple wireless relay nodes, are responsible for collecting and sending the signals from the wireless sensors to the base station. The data is stored and processed using intelligent techniques in a cloud-based environment. Early warning alerts are automatically sent to the medical staff allowing them to intervene on time and earlier than when using manual vital sign observations. By grouping patients according to risk, medical experts can continuously monitor patients health status and quickly identify those that need their attention most.},
eventtitle = {2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference ({SEEDA}-{CECNSM})},
pages = {1--6},
booktitle = {2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference ({SEEDA}-{CECNSM})},
author = {Karvounis, Evaggelos and Vavva, Maria and Giannakeas, Nikolaos and Tzallas, Alexandros T. and Smanis, Ioannis and Tsipouras, Markos G.},
date = {2021-09},
keywords = {Internet of Things, Wireless Sensor Network, Temperature measurement, Temperature sensors, Wireless communication, Wireless sensor networks, Hospitals, Artificial Intelligence ({AI}), component, Electronic healthcare, health monitoring, Sensor systems, Smart healthcare, ubiquitous computing, wearable devices},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/IDTXE2ZS/9566252.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/S2WR9JTE/Karvounis et al. - 2021 - A Hospital Healthcare Monitoring System Using Inte.pdf:application/pdf},
series = {{EAI}/Springer Innovations in Communication and Computing},
abstract = {The Internet of Things ({IoT}) is the heart of intelligent healthcare, which is very useful because the accessibility of the internet is worldwide. This chapter begins by explaining the difference between Vital Signs Early Detection ({VSED}) and Physical Signs Early Detection ({PSED}), and it shows the relationship between {VSED} and {PSED}. This chapter concludes by outlining measuring systems of various chronic disease parameters using the latest technology – the {IoT} and {ThingSpeak}. This chapter aimed to develop a low cost system which will be easily connected with any kind of digital medical sensor and remotely provide the measured readings to medical professionals globally, thereby assisting with the early detection of chronic diseases. The various parameters are blood pressure, which is measured according to systolic and diastolic parameters, pulse rate and body temperature. The measuring system is very simple; it is plugged in and sends the measured parameter reading to the cloud, and it is accessible to the recipient who has the {ID} and password. Medical professionals will obtain the reading and analyse it properly, and on the basis of that analysis decide whether the patient requires hospitalization. This chapter outlines a new way to observe patients and their needs within the comfort of their own homes, and it indicates ways to predict chronic diseases on the basis of the early warning score ({EWS}) from the basic parameter readings. To obtain the {EWS}, readings are compared with the previous patient data and changes are used to calculate the {EWS}.},
pages = {19--31},
booktitle = {Intelligent Healthcare: Applications of {AI} in {eHealth}},
publisher = {Springer International Publishing},
author = {Kale, Yogesh S. and Rathkanthiwar, Shubhangi V. and Gawande, Prachi D.},
editor = {Bhatia, Surbhi and Dubey, Ashutosh Kumar and Chhikara, Rita and Chaudhary, Poonam and Kumar, Abhishek},
abstract = {Measuring the physiological parameters and their variations for 24 h provides a significantly visible pattern for patient health monitoring. The life-threatening event can be identified by determining the abnormalities in physiological parameters. These parameters play a vital role in the accurate calculation of early warning score ({EWS}). Therefore, in this paper, an automated and internet of things ({IoT})-based {EWS} calculation has been presented for timely detection and diagnosis of the abnormality associated with the physiological parameters of the patient. It will be useful in avoiding life-threatening events. At present, the {EWS} is calculated by the conventional method, where the different physiological parameters are monitored by medical staff or nurses in a particular time interval. The conventional scoring system is laborious, time-consuming, and prone to human error. Thus, the presented automatic and {IoT}-based {EWS} system for patient monitoring has been successfully implemented. The presented system can continuously calculate {EWS} by measuring and recording the physiological parameter in real-time. A laboratory prototype has been configured and validated for the {EWS} calculation and observation in the hospital and home environment.},
pages = {1--12},
number = {0},
journaltitle = {{IETE} Journal of Research},
author = {Sahu, Manju Lata and Atulkar, Mithilesh and Ahirwal, Mitul Kumar and Ahamad, Afsar},
keywords = {Early warning score, Sensors, Physiological parameters, Internet of things, Automated {EWS}, In-home system},
file = {Full Text PDF:/home/ulinja/Zotero/storage/2JFXM2RX/Sahu et al. - 2022 - Internet-of-Things-Enabled Early Warning Score Sys.pdf:application/pdf},
}
@article{shaik_remote_2023,
title = {Remote patient monitoring using artificial intelligence: Current state, applications, and challenges},
shorttitle = {Remote patient monitoring using artificial intelligence},
abstract = {The adoption of artificial intelligence ({AI}) in healthcare is growing rapidly. Remote patient monitoring ({RPM}) is one of the common healthcare applications that assist doctors to monitor patients with chronic or acute illness at remote locations, elderly people in-home care, and even hospitalized patients. The reliability of manual patient monitoring systems depends on staff time management which is dependent on their workload. Conventional patient monitoring involves invasive approaches which require skin contact to monitor health status. This study aims to do a comprehensive review of {RPM} systems including adopted advanced technologies, {AI} impact on {RPM}, challenges and trends in {AI}-enabled {RPM}. This review explores the benefits and challenges of patient-centric {RPM} architectures enabled with Internet of Things wearable devices and sensors using the cloud, fog, edge, and blockchain technologies. The role of {AI} in {RPM} ranges from physical activity classification to chronic disease monitoring and vital signs monitoring in emergency settings. This review results show that {AI}-enabled {RPM} architectures have transformed healthcare monitoring applications because of their ability to detect early deterioration in patients' health, personalize individual patient health parameter monitoring using federated learning, and learn human behavior patterns using techniques such as reinforcement learning. This review discusses the challenges and trends to adopt {AI} to {RPM} systems and implementation issues. The future directions of {AI} in {RPM} applications are analyzed based on the challenges and trends. This article is categorized under: Application Areas {\textgreater} Health Care Technologies {\textgreater} Artificial Intelligence Technologies {\textgreater} Internet of Things},
pages = {e1485},
number = {2},
journaltitle = {{WIREs} Data Mining and Knowledge Discovery},
author = {Shaik, Thanveer and Tao, Xiaohui and Higgins, Niall and Li, Lin and Gururajan, Raj and Zhou, Xujuan and Acharya, U. Rajendra},
file = {Full Text PDF:/home/ulinja/Zotero/storage/WUD6AIM4/Shaik et al. - 2023 - Remote patient monitoring using artificial intelli.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/JUM4HJDC/widm.html:text/html},
}
@inproceedings{quraishi_internet_2021,
title = {Internet of Things in Healthcare, A Literature Review},
doi = {10.1109/ICTAI53825.2021.9673369},
abstract = {Over the last few decades, extensive research has been consigned to the study of various technologies such as information technologies in order to strengthen the existing ones. One such field is the Internet of things ({IoT}). In recent times, internet of things has been a ground-breaking technology in almost all sectors of human life. {IoT} holds the tendency to make available both value-added services and mainstream services in all fields. Healthcare sector is one such area. {IoT} for healthcare tends to keep doctors and professionals more watchful and connected with the patients proactively. For the overall development of a nation, healthcare is the main service center. It is clear that technology cannot help to eradicate the issues related to health completely but it can make access to healthcare easier. In today’s scenario where healthcare has been drastically despoiled, {IoT} has completely changed the perspective of traditional healthcare methods. {IoT} tends to play a crucial role in providing better and improved services. Real-time monitoring of the data via smart devices use to transfer collected health data to a physician. Patient health observation and monitoring, emergency proceedings, remote inspection, and observation are some vital analyses that can be made with the launch of the Internet of Things in the respected sector. The standpoint of the paper is to summarize the applications of {IoT} in the field of the healthcare field.},
eventtitle = {2021 International Conference on Technological Advancements and Innovations ({ICTAI})},
pages = {198--202},
booktitle = {2021 International Conference on Technological Advancements and Innovations ({ICTAI})},
author = {Quraishi, Suhail Javed and Yusuf, Humra},
date = {2021-11},
keywords = {Internet of Things, Healthcare, Medical services, Sensors, {IoT}, Bibliographies, Information technologies, Inspection, Real-time systems, Remote inspection, Smart devices, Technological innovation},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/KZGMR5L4/9673369.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/KR2C82FM/Quraishi and Yusuf - 2021 - Internet of Things in Healthcare, A Literature Rev.pdf:application/pdf},
title = {Review on {IoT} based Healthcare systems},
doi = {10.1109/ICACTA54488.2022.9753547},
abstract = {The world is moving to Internet of Things ({IoT}) remote monitoring technology and quick control of objects as well more precisely. {IoT} can be beneficial in medicine as well health care facilities as it allows for long-term research of chronic diseases, vital symptom monitoring, emergency perception, diagnosis and prediction of patient level or disease. Internet of Things becomes transparent and is useful in the context of health care and venerable care, i.e., in most cases, they are activities that require the full presence of a caretaker or medical personal. The purpose of this survey is to enlighten the significance and categories of {IoT}-include adult health care programs. This paper consolidates a summary of research that report on the development and use of {IoT}-include health care adult programs. The paper covers with various available {IoT} based techniques used for health care applications.},
eventtitle = {2022 International Conference on Advanced Computing Technologies and Applications ({ICACTA})},
pages = {1--5},
booktitle = {2022 International Conference on Advanced Computing Technologies and Applications ({ICACTA})},
author = {B V, Santhosh Krishna and Sharma, Sanjeev and Swathi, Kurapati Sai and Yamini, Korapati Reddy and Kiran, Chokkam Preethi and Chandrika, Kamineni},
date = {2022-03},
keywords = {Internet of Things, Medical services, Monitoring, Security, Diagnosis, Electrocardiography, Encryption, Heart, Internet of Things [{IoT}], Perception, Productivity},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/MY7DTWBQ/9753547.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/3VPC4T36/B V et al. - 2022 - Review on IoT based Healthcare systems.pdf:application/pdf},
title = {A data fusion algorithm for clinically relevant anomaly detection in remote health monitoring},
doi = {10.1109/ISNCC49221.2020.9297315},
abstract = {The adoption of the Internet of Things ({IoT}) technologies in healthcare lead to the Healthcare 4.0 paradigm. In this paradigm, Remote Health Monitoring ({RHM}) applications emerge to provide continuous monitoring of patient's health conditions. But {RHM} applications traditionally present high rates of false alarms. This disturbance is caused by many factors, from the high sensibility of the equipments to real variations in the monitored vital signs not related to emergencies of health degradation. Hence, this work proposes a system for detection and evaluation of medical emergencies, using Wireless Body Sensor Network as its network infrastructure, able to distinguish real emergencies from other cases by considering a risk estimation from each sampled data. Experiments showed that the proposed system can reach an average accuracy rate of 93.0\% and detection rate of 87.2\%, and an energy consumption profile feasible to {WBSN} contexts.},
eventtitle = {2020 International Symposium on Networks, Computers and Communications ({ISNCC})},
pages = {1--8},
booktitle = {2020 International Symposium on Networks, Computers and Communications ({ISNCC})},
author = {de Mello Dantas, Hugo and Miceli de Farias, Claudio},
date = {2020-10},
keywords = {Internet of Things, Biomedical monitoring, Medical services, Monitoring, Wireless communication, Data integration, Emergency Detection, Remote Health Monitoring, Uncertainty, Wireless Body Sensor Networks},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/ZPAGY7ER/9297315.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/EIHD9N7X/de Mello Dantas and Miceli de Farias - 2020 - A data fusion algorithm for clinically relevant an.pdf:application/pdf},
title = {Context-Aware Early Warning System for In-Home Healthcare Using Internet-of-Things},
isbn = {978-3-319-47063-4},
doi = {10.1007/978-3-319-47063-4_56},
series = {Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering},
abstract = {Early warning score ({EWS}) is a prediction method to notify caregivers at a hospital about the deterioration of a patient. Deterioration can be identified by detecting abnormalities in patient’s vital signs several hours prior the condition of the patient gets life-threatening. In the existing {EWS} systems, monitoring of patient’s vital signs and the determining the score is mostly performed in a paper and pen based way. Furthermore, currently it is done solely in a hospital environment. In this paper, we propose to import this system to patients’ home to provide an automated platform which not only monitors patents’ vital signs but also looks over his/her activities and the surrounding environment. Thanks to the Internet-of-Things technology, we present an intelligent early warning method to remotely monitor in-home patients and generate alerts in case of different medical emergencies or radical changes in condition of the patient. We also demonstrate an early warning score analysis system which continuously performs sensing, transferring, and recording vital signs, activity-related data, and environmental parameters.},
pages = {517--522},
booktitle = {Internet of Things. {IoT} Infrastructures},
publisher = {Springer International Publishing},
author = {Anzanpour, Arman and Rahmani, Amir-Mohammad and Liljeberg, Pasi and Tenhunen, Hannu},
editor = {Mandler, Benny and Marquez-Barja, Johann and Mitre Campista, Miguel Elias and Cagáňová, Dagmar and Chaouchi, Hakima and Zeadally, Sherali and Badra, Mohamad and Giordano, Stefano and Fazio, Maria and Somov, Andrey and Vieriu, Radu-Laurentiu},
series = {The 7th International Conference on Ambient Systems, Networks and Technologies ({ANT} 2016) / The 6th International Conference on Sustainable Energy Information Technology ({SEIT}-2016) / Affiliated Workshops},
abstract = {The increased use of mobile technologies and smart devices in the area of health has caused great impact on the world. Health experts are increasingly taking advantage of the benefits these technologies bring, thus generating a significant improvement in health care in clinical settings and out of them. Likewise, countless ordinary users are being served from the advantages of the M-Health (Mobile Health) applications and E-Health (health care supported by {ICT}) to improve, help and assist their health. Applications that have had a major refuge for these users, so intuitive environment. The Internet of things is increasingly allowing to integrate devices capable of connecting to the Internet and provide information on the state of health of patients and provide information in real time to doctors who assist. It is clear that chronic diseases such as diabetes, heart and pressure among others, are remarkable in the world economic and social level problem. The aim of this article is to develop an architecture based on an ontology capable of monitoring the health and workout routine recommendations to patients with chronic diseases.},
pages = {90--97},
journaltitle = {Procedia Computer Science},
shortjournal = {Procedia Computer Science},
author = {Gómez, Jorge and Oviedo, Byron and Zhuma, Emilio},
urldate = {2023-04-26},
date = {2016-01-01},
langid = {english},
keywords = {Internet of Things, E-Health, Context Awareness, Ontology},
file = {ScienceDirect Full Text PDF:/home/ulinja/Zotero/storage/LDZVGYUT/Gómez et al. - 2016 - Patient Monitoring System Based on Internet of Thi.pdf:application/pdf;ScienceDirect Snapshot:/home/ulinja/Zotero/storage/XH3BU4JZ/S1877050916301260.html:text/html},
title = {An {IoT} based system for remote patient monitoring},
doi = {10.1109/CarpathianCC.2016.7501056},
abstract = {Following a surgical procedure, patients are monitored in an {ICU} until physically stable, after which are discharged to a ward for further evaluation and recovery. Usually, ward evaluation does not imply continuous physiological parameters monitoring and therefore patient relapse is not uncommon. The present paper describes the steps taken to design and build a low-cost modular monitoring system prototype. This system aims to offer mobile support in order to facilitate faster and better medical interventions in emergency cases and has been developed using low-power dedicated sensor arrays for {EKG}, {SpO}2, temperature and movement. The interfaces for these sensors have been developed according to the {IoT} model: a central control unit exposes a {RESTful} based Web interface that ensures a platform agnostic behaviour and provides a flexible mechanism to integrate new components.},
eventtitle = {2016 17th International Carpathian Control Conference ({ICCC})},
pages = {1--6},
booktitle = {2016 17th International Carpathian Control Conference ({ICCC})},
author = {Archip, Alexandru and Botezatu, Nicolae and Şerban, Elena and Herghelegiu, Paul-Corneliu and Zală, Andrei},
date = {2016-05},
keywords = {Internet of Things, Biomedical monitoring, Monitoring, Remote patient monitoring, Internet of things, Temperature sensors, Prototypes, Electrocardiography, E-health, Embedded Systems, Logic gates, {RESTful} Web Services},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/GR5KW752/7501056.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/SJIJNI7I/Archip et al. - 2016 - An IoT based system for remote patient monitoring.pdf:application/pdf},
title = {An Efficient Wireless Health Monitoring System},
doi = {10.1109/I-SMAC.2018.8653716},
abstract = {Until now the exact method of monitoring patients in hospitals is to make the patients lay down on the bed, connect appropriate sensors for monitoring. This method of monitoring may be uncomfortable due to various reasons. To monitor the increased number of patients, a large number of trained medical professionals and doctors are required. Due to this shortage of trained medical professionals, uninterrupted patient monitoring systems have drawn significant attention during the current decade. A large number of cost-effective versions of patient monitoring systems are available, which were being utilized by authorized health care professionals. In addition to this there is a strong need for web based patient monitoring system using internet of things ({IoT}) technologies, when the patient is not in the hospital. The goal of this paper is to design and implement a low cost, portable effective patient monitoring system that can transmit the vital signs of a patient in emergency situation continuously through a wireless communication network system. Various sensors such as pulse, temperature, blood pressure and fingerprint are interfaced with the microcontroller for measuring the important physical parameters of a patient. For wireless transmission, these sensors are connected to a sensor node through {GSM} module. Raspberry-Pi is used as a sensor node as it has better features compared to the other microcontrollers. The developed system includes hardware such as Raspberry Pi 3 model B, blood pressure sensor, temperature, Pulse-oximeters and {GSM} module. The developed hardware prototype was tested successfully on four patients and satisfactory readings have been monitored on the display.},
eventtitle = {2018 2nd International Conference on 2018 2nd International Conference on I-{SMAC} ({IoT} in Social, Mobile, Analytics and Cloud) (I-{SMAC})I-{SMAC} ({IoT} in Social, Mobile, Analytics and Cloud) (I-{SMAC})},
pages = {373--377},
booktitle = {2018 2nd International Conference on 2018 2nd International Conference on I-{SMAC} ({IoT} in Social, Mobile, Analytics and Cloud) (I-{SMAC})I-{SMAC} ({IoT} in Social, Mobile, Analytics and Cloud) (I-{SMAC})},
author = {Chowdary, Kovuru Chandu and Lokesh Krishna, K. and Prasad, K Lalu and Thejesh, K.},
date = {2018-08},
keywords = {Biomedical monitoring, Medical services, Monitoring, Temperature measurement, Temperature sensors, Blood pressure, Remote monitoring, and {IoT}, Blood flow rate, {GSM}, Microcontroller, temperature Sensor node},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/AW9IDIT6/8653716.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/GCRHYVX8/Chowdary et al. - 2018 - An Efficient Wireless Health Monitoring System.pdf:application/pdf},
title = {Design and Development of {IOT} Based Multi-Parameter Patient Monitoring System},
doi = {10.1109/ICACCS48705.2020.9074293},
abstract = {Multi-parameter patient monitor captures the physiological vital signs and continuously monitors the patient condition by alerting the medical staff via alarm. The revolutionization in machine learning techniques and Internet of Things ({IOT}) in healthcare. In this paper, we have designed an {IOT} based {MPM} system where four parameter namely heart rate, respiration rate, oxygen saturation and temperature are monitored using corresponding sensors and an email is sent to patient's guardian in case of emergency. The project also focuses on improving the performance of {MPM} system using Support Vector Machine({SVM}) algorithm. The classification accuracy of 95\% has been achieved.},
eventtitle = {2020 6th International Conference on Advanced Computing and Communication Systems ({ICACCS})},
pages = {862--866},
booktitle = {2020 6th International Conference on Advanced Computing and Communication Systems ({ICACCS})},
author = {Athira, A. and Devika, T.D. and Varsha, K.R. and Bose S., Sree Sanjanaa},
date = {2020-03},
note = {{ISSN}: 2575-7288},
keywords = {Biomedical monitoring, Medical services, Monitoring, Heart rate, Temperature measurement, Temperature sensors, {IOT}, {MPM}, Smart Health, {SVM} classifier},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/3CAVK8H5/9074293.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/Y852V2DN/Athira et al. - 2020 - Design and Development of IOT Based Multi-Paramete.pdf:application/pdf},
abstract = {Early warning score ({EWS}) is an approach to detect the deterioration of a patient. It is based on a fact that there are several changes in the physiological parameters prior a clinical deterioration of a patient. Currently, {EWS} procedure is mostly used for in-hospital clinical cases and is performe},
pages = {174--177},
number = {8},
journaltitle = {{EAI} Endorsed Transactions on Internet of Things},
author = {Anzanpour, Arman and Rahmani, Amir-Mohammad and Liljeberg, Pasi and Tenhunen, Hannu},
urldate = {2023-04-26},
date = {2015-12-22},
file = {Full Text PDF:/home/ulinja/Zotero/storage/MUEL539U/Anzanpour et al. - 2015 - Internet of Things Enabled In-Home Health Monitori.pdf:application/pdf},
title = {Medical warning system based on Internet of Things using fog computing},
doi = {10.1109/IWBIS.2016.7872884},
abstract = {Remote patient monitoring is essential for many patients that are suffering from acute diseases such as different heart conditions. Continuous health monitoring can provide medical services that consider the current medical state of the patient and to predict or early-detect future potentially critical situations. In this regard, Internet of Things as a multidisciplinary paradigm can provide profound impacts. However, the current {IoT}-based systems may encounter difficulties to provide continuous and real time patient monitoring due to issues in data analytics. In this paper, we introduce a new {IoT}-based approach to offer smart medical warning in personalized patient monitoring. The proposed approach consider local computing paradigm enabled by machine learning algorithms and automate management of system components in computing section. The proposed system is evaluated via a case study concerning continuous patient monitoring to early-detect patient deterioration via arrhythmia in {ECG} signal.},
eventtitle = {2016 International Workshop on Big Data and Information Security ({IWBIS})},
pages = {19--24},
booktitle = {2016 International Workshop on Big Data and Information Security ({IWBIS})},
author = {Azimi, Iman and Anzanpour, Arman and Rahmani, Amir M. and Liljeberg, Pasi and Salakoski, Tapio},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/NCEXJGHU/7872884.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/B4P7DF44/Azimi et al. - 2016 - Medical warning system based on Internet of Things.pdf:application/pdf},
title = {Adopting the Internet of Things technologies in health care systems},
doi = {10.1109/ICEPE.2014.6969965},
abstract = {Internet of Things based health care systems play a significant role in Information and Communication Technologies and has contribution in development of medical information systems. The developing of {IoT}-based health care systems must ensure and increase the safety of patients, the quality of life and other health care activities. The tracking, tracing and monitoring of patients and health care actors activities are challenging research directions. In this paper we propose a general architecture of a health care system for monitoring of patients at risk in smart Intensive Care Units. The system advices and alerts in real time the doctors/medical assistants about the changing of vital parameters or the movement of the patients and also about important changes in environmental parameters, in order to take preventive measures.},
eventtitle = {2014 International Conference and Exposition on Electrical and Power Engineering ({EPE})},
pages = {532--535},
booktitle = {2014 International Conference and Exposition on Electrical and Power Engineering ({EPE})},
author = {Chiuchisan, Iuliana and Costin, Hariton-Nicolae and Geman, Oana},
date = {2014-10},
keywords = {Internet of Things, Biomedical monitoring, Medical services, Monitoring, Temperature measurement, Temperature sensors, health care system, internet of things, Kinect, sensors, smart environment},
file = {IEEE Xplore Abstract Record:/home/ulinja/Zotero/storage/GC7RHLL2/6969965.html:text/html;IEEE Xplore Full Text PDF:/home/ulinja/Zotero/storage/8CZGFIAN/Chiuchisan et al. - 2014 - Adopting the Internet of Things technologies in he.pdf:application/pdf},
title = {The {OAuth} 2.0 Authorization Framework},
url = {https://datatracker.ietf.org/doc/rfc6749},
abstract = {The {OAuth} 2.0 authorization framework enables a third-party application to obtain limited access to an {HTTP} service, either on behalf of a resource owner by orchestrating an approval interaction between the resource owner and the {HTTP} service, or by allowing the third-party application to obtain access on its own behalf. This specification replaces and obsoletes the {OAuth} 1.0 protocol described in {RFC} 5849. [{STANDARDS}-{TRACK}]},
number = {{RFC} 6749},
institution = {Internet Engineering Task Force},
type = {Request for Comments},
author = {Hardt, Dick},
urldate = {2023-08-21},
date = {2012-10},
doi = {10.17487/RFC6749},
note = {Num Pages: 76},
file = {Full Text PDF:/home/ulinja/Zotero/storage/978WTBV3/Hardt - 2012 - The OAuth 2.0 Authorization Framework.pdf:application/pdf},
}
@online{noauthor_gotify_nodate,
title = {Gotify · a simple server for sending and receiving messages},
url = {https://gotify.net/},
abstract = {a simple server for sending and receiving messages},
abstract = {Oxygen saturation is an essential element in the management and understanding of patient care. Oxygen is tightly regulated within the body because hypoxemia can lead to many acute adverse effects on individual organ systems. These include the brain, heart, and kidneys. Oxygen saturation measures how much hemoglobin is currently bound to oxygen compared to how much hemoglobin remains unbound. At the molecular level, hemoglobin consists of four globular protein subunits. Each subunit is associated with a heme group. Each molecule of hemoglobin subsequently has four heme-binding sites readily available to bind oxygen. Therefore, during the transport of oxygen in the blood, hemoglobin is capable of carrying up to four oxygen molecules. Due to the critical nature of tissue oxygenconsumption in the body, it is essential to be able to monitor current oxygen saturation. A pulse oximeter can measure oxygen saturation. It is a noninvasive device placed over a person's finger. It measures light wavelengths to determine the ratio of the current levels of oxygenated hemoglobin to deoxygenated hemoglobin. The use of pulse oximetry has become a standard of care in medicine. Itis often regarded as a fifth vital sign. As such, medical practitioners must understand the functions and limitations of pulse oximetry. They should also have a basic knowledge of oxygen saturation.},
title = {The world’s first analog watch with clinically validated {ECG} - {ScanWatch} {\textbar} Withings},
url = {https://www.withings.com/de/en/scanwatch},
abstract = {The hybrid smartwatch that detects atrial fibrillation, with {ECG} and an oximeter for {SpO}2 measurements—plus activity and sleep tracking.},
abstract = {Disclaimer: Due to health regulations and clearances, some {ScanWatch} features may not be available or may not have clinical validation in your region. Learn more.
abstract = {The first Wi-Fi blood pressure monitor with {ECG} and a digital stethoscope to detect cardiovascular conditions like atrial fibrillation and valvular heart disease.},
abstract = {Note:With Firmware Release 841, we have introduced an optional Heart Sound Measurement Assistance Mode which can provide real-time feedback for correct positioning. Read about it here.
