feat(proposal): update objectives
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@ -397,22 +397,6 @@ Publisher: Multidisciplinary Digital Publishing Institute},
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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},
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}
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@article{subbe_validation_2001,
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title = {Validation of a modified Early Warning Score in medical admissions},
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volume = {94},
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issn = {1460-2725},
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url = {https://doi.org/10.1093/qjmed/94.10.521},
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doi = {10.1093/qjmed/94.10.521},
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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.},
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pages = {521--526},
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number = {10},
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journaltitle = {{QJM}: An International Journal of Medicine},
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author = {Subbe, C.P. and Kruger, M. and Rutherford, P. and Gemmel, L.},
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urldate = {2023-04-26},
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date = {2001-10-01},
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file = {Full Text PDF:/home/ulinja/Zotero/storage/GC4BT2WU/Subbe et al. - 2001 - Validation of a modified Early Warning Score in me.pdf:application/pdf;Snapshot:/home/ulinja/Zotero/storage/KHAD42Z5/1558977.html:text/html},
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}
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@article{sahu_vital_2022,
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title = {Vital Sign Monitoring System for Healthcare Through {IoT} Based Personal Service Application},
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volume = {122},
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@ -1070,3 +1054,37 @@ Pre-hospital {NEWS} was associated with death or critical care unit escalation w
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keywords = {Clinical research, Acute care emergency ambulance systems, Intensive care, Pre-hospital},
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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},
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}
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@article{subbe_validation_2001,
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title = {Validation of a modified Early Warning Score in medical admissions},
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volume = {94},
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issn = {1460-2725},
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url = {https://doi.org/10.1093/qjmed/94.10.521},
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doi = {10.1093/qjmed/94.10.521},
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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.},
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pages = {521--526},
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number = {10},
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journaltitle = {{QJM}: An International Journal of Medicine},
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author = {Subbe, C.P. and Kruger, M. and Rutherford, P. and Gemmel, L.},
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urldate = {2023-04-30},
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date = {2001-10-01},
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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},
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}
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@inproceedings{kim_two_2007,
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location = {Berlin, Heidelberg},
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title = {Two Algorithms for Detecting Respiratory Rate from {ECG} Signal},
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isbn = {978-3-540-36841-0},
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doi = {10.1007/978-3-540-36841-0_1030},
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series = {{IFMBE} Proceedings},
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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\%.},
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pages = {4069--4071},
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booktitle = {World Congress on Medical Physics and Biomedical Engineering 2006},
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publisher = {Springer},
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author = {Kim, J. M. and Hong, J. H. and Kim, N. J. and Cha, E. J. and Lee, Tae-Soo},
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editor = {Magjarevic, R. and Nagel, J. H.},
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date = {2007},
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langid = {english},
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keywords = {{ECG}, {EDR}, {QRS}, R-R interval},
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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},
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}
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@ -3,15 +3,18 @@
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\usepackage[utf8]{inputenc}
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\usepackage[T1]{fontenc}
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%\usepackage[english, ngerman]{babel}
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\usepackage{csquotes}
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\usepackage[english]{babel}
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\usepackage{graphicx}
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\usepackage{parskip}
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\usepackage{caption}
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\usepackage{subcaption}
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\usepackage{adjustbox}
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\usepackage{nicematrix}
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\usepackage{fancyhdr}
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\usepackage{blindtext}
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\usepackage[left=1cm, right=1cm, top=1.5cm, bottom=1.5cm]{geometry}
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\usepackage[table]{xcolor}
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%\usepackage[table]{xcolor}
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\usepackage{color}
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\usepackage[colorlinks]{hyperref}
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\pagestyle{plain}
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@ -30,9 +33,10 @@
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\input{cover.tex}
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\section{Background}
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Medical deterioration is a critical concern in healthcare, particularly for vulnerable populations such as the elderly and chronically
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Clinical deterioration is a critical concern in healthcare, particularly for vulnerable populations such as the elderly and chronically
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ill patients. It refers to a decline in a patient's health status and may lead to adverse outcomes, including hospitalization,
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longer stays in intensive care units, and increased healthcare costs.
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Early warning scores (EWS) have been widely adopted internationally for early detection of deteriorating patients\cite{downey_strengths_2017}.
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@ -44,6 +48,31 @@ Two common implementations are the \textit{National Early Warning Score 2} (NEWS
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\textit{Modified Early Warning Score} (MEWS)\cite{burgos-esteban_effectiveness_2022}.
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Both are calculated by capturing various vital parameters from the patient at a specific point in time, followed by numerical aggregation of the
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captured data according to the specifically used score.
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For MEWS, each type of vitals parameter is assigned an individual score based on which range it is in.
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The individual scores are then added together to produce the final MEWS.
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The ranges for individual scores for each type of vital parameter is shown in table \ref{mews-table}.
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\begin{table}[!h]
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\noindent\adjustbox{max width=\textwidth}{
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\begin{NiceTabular}{l>{\columncolor{red!15}}c>{\columncolor{orange!15}}c>{\columncolor{yellow!15}}c>{\columncolor{green!15}}c>{\columncolor{yellow!15}}c>{\columncolor{orange!15}}c>{\columncolor{red!15}}c}[hvlines,colortbl-like]
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\hline
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& $\mathbf{+3}$ & $\mathbf{+2}$ & $\mathbf{+1}$ & $\mathbf{+0}$ & $\mathbf{+1}$ & $\mathbf{+2}$ & $\mathbf{+3}$ \\
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\hline
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Systolic Blood Pressure [mmHg] & $<70$ & $71-80$ & $81-100$ & $101-199$ & & $\geq 200$ & \\
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\hline
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Heart Rate [bpm] & & $<40$ & $41-50$ & $51-100$ & $101-110$ & $111-129$ & $\geq 130$ \\
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\hline
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Respiratory Rate [bpm] & & $<9$ & & $9-14$ & $15-20$ & $21-29$ & $\geq 30$ \\
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\hline
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Temperature [°C] & & $<35$ & & $35-38.4$ & & $\geq 38.5$ & \\
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\hline
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AVPU score & & & & alert & reacting to voice & reacting to pain & unresponsive \\
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\hline
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\end{NiceTabular}
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}
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\caption{\label{mews-table}MEWS calculation thresholds}
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\end{table}
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Traditionally, doctors and nursing staff perform collection and evaluation of the data manually, inputting data into an EWS-calculator by hand.
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Low frequency of scoring, miscalculations and practical integration are known setbacks of NEWS2 and other scores\cite{eisenkraft_developing_2023}.
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@ -58,8 +87,10 @@ commercially available\cite{noauthor_visi_nodate, noauthor_equivital_nodate, noa
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%Javanbakht et al. found that continuous vitals monitoring is more cost-effective than intermittent monitoring\cite{javanbakht_cost_2020}, however the findings of this study should be taken lightly due to potential bias reporting.
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\section{Motivation}
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% TODO EWS makes prediction value better than monitoring abnormalities in single vital signs
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Installing and operating traditional continuous monitoring systems, like the vital sign monitors used in medical facilities, demands
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specialized equipment and technical expertise.
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Furthermore, these systems are cumbersome for patients, as they involve connecting patient and sensor device with numerous electrodes
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@ -87,17 +118,49 @@ for patients at risk of deterioration, both in their homes and on the go.
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%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\cite{downey_patient_2018}.
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%Taking continuous measurements is superior to measuring intermittently\cite{gronbaek_continuous_2023, shaik_remote_2023}.
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\section{Objectives}
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The objective of this research is to explore the feasibility of using an existing EWS for dismissed patients who are still at risk
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of deterioration.
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design and develop a web application that can capture and process vitals data from a wide range of
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smart medical sensors, and accurately calculate the Modified Early Warning Score (MEWS) based on the captured data.
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The application will be aimed at providing a mobile early warning system for patients at risk of deterioration, by providing real-time
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data and alerts to medical professionals.
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The proposed research will involve development of a robust and user-friendly web application interface.
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The ultimate goal of this research is to provide a tool that can effectively monitor and predict medical deterioration,
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thereby improving patient outcomes and reducing healthcare costs.
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The objective of this research is to explore the practical feasibility of using an existing, clinically validated EWS to remotely monitor
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patients who are still at risk of deterioration after having been dismissed from medical care facilities,
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utilizing smart medical sensor devices.
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Taking measurements using the devices should be as easy and unintrusive as possible for the patient, enabling them to take
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vital sign readings easily from the comfort of their home or while out of the house.
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MEWS will be used as an EWS for deterioration monitoring.
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Furthermore, individual vital signs will be monitored for abnormalities.
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The following vital signs will be captured:
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\begin{itemize}
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\item Heart Rate (HR)
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\item Blood Pressure (BP)
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\item Respiratory Rate (RR)
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\item Body Temperature (TEMP)
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\item Blood Oxygen Saturation (SPO2)
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\item AVPU Score
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\end{itemize}
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The following smart medical devices will be used to take vital sign measurements:
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\begin{table}[!h]
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\noindent\adjustbox{max width=\textwidth}{
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\begin{NiceTabular}{lll}[hvlines,colortbl-like]
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\hline
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\textbf{Device Name} & \textbf{Device Type} & \textbf{Captured Vitals Parameter} \\
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\hline
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\href{https://www.withings.com/de/en/scanwatch}{Withings Scanwatch} & Wearable Smartwatch & HR, SPO2, RR (sleeping) \\
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\hline
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\href{https://www.withings.com/de/en/thermo}{Withings Thermo} & Handheld Smart Thermometer & TEMP \\
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\hline
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\href{https://www.withings.com/de/en/bpm-core}{Withings BPM Core} & Smart Blood Pressure Cuff & BP, HR \\
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\hline
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\end{NiceTabular}
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}
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\caption{\label{device-table}Smart Devices used for data capture}
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\end{table}
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This will be accomplished by designing and developing a web application that can capture and process vitals data from a wide range of
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smart medical sensors, and accurately calculates the MEWS based on the captured data.
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If the calculated value lies outside of the acceptable MEWS threshold, both the patient and medical staff can be alerted,
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allowing preemptive action to be taken.
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\section{Tasks}
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