docs(proposal): add literature review
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@ -27,3 +27,6 @@ docs/proposal/*.pdf
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# Drawio backup and lock files
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**/*.drawio.bkp
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**/*.drawio.dtmp
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# Libreoffice lock files
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**/.~lock*
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@ -35,7 +35,7 @@
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eventtitle = {{MOBIHEALTH} 2015 - 5th {EAI} International Conference on Wireless Mobile Communication and Healthcare - Transforming Healthcare through Innovations in Mobile and Wireless Technologies},
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author = {Anzanpour, A. and Rahmani, A.-M. and Liljeberg, P. and Tenhunen, H.},
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date = {2015},
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keywords = {Internet of Things, Body Area Network, {EarlyWarning} Score, Remote Patient Monitoring, Wearable electronics, Wireless Sensor Network},
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keywords = {Body Area Network, {EarlyWarning} Score, Internet of Things, Remote Patient Monitoring, Wearable electronics, Wireless Sensor Network},
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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},
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}
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@ -50,7 +50,7 @@
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journaltitle = {Clinical and Experimental Emergency Medicine},
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author = {Dagan, A. and Mechanic, O.J.},
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date = {2020},
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keywords = {Internet of Things, Fitness trackers, Global health, Monitoring, physiologic, Telemedicine},
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keywords = {Fitness trackers, Global health, Internet of Things, Monitoring, physiologic, Telemedicine},
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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},
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}
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@ -84,7 +84,7 @@
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journaltitle = {Instrumentation Mesure Metrologie},
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author = {Thippeswamy, V.S. and Shivakumaraswamy, P.M. and Chickaramanna, S.G. and Iyengar, V.M. and Das, A.P. and Sharma, A.},
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date = {2021},
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keywords = {Internet of things, {ECG}, Heart rate, {ICU}, Real-time monitoring, {SpO}2, Vital signs},
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keywords = {{ECG}, Heart rate, {ICU}, Internet of things, Real-time monitoring, {SpO}2, Vital signs},
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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},
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}
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@ -97,7 +97,7 @@
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booktitle = {2020 Second International Conference on Inventive Research in Computing Applications ({ICIRCA})},
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author = {Yeri, Vani and Shubhangi, D.C.},
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date = {2020-07},
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keywords = {Cloud computing, {IoT}, Arduino, Health, Medical services, monitoring, Monitoring, patient, sensor, Temperature measurement, Temperature sensors, wireless, Wireless communication, Wireless sensor networks},
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keywords = {Arduino, Cloud computing, Health, {IoT}, Medical services, monitoring, Monitoring, patient, sensor, Temperature measurement, Temperature sensors, wireless, Wireless communication, Wireless sensor networks},
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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},
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}
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@ -140,7 +140,7 @@
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journaltitle = {International Journal of Nursing Studies},
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author = {Downey, C.L. and Tahir, W. and Randell, R. and Brown, J.M. and Jayne, D.G.},
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date = {2017},
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keywords = {Vital signs, Early warning scores, Limitations, Strengths, Systematic review},
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keywords = {Early warning scores, Limitations, Strengths, Systematic review, Vital signs},
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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},
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}
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@ -200,7 +200,7 @@ Publisher: Nature Publishing Group},
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pmid = {30580650},
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note = {Publisher: Taylor \& Francis
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\_eprint: https://doi.org/10.1080/17434440.2019.1563480},
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keywords = {patient deterioration, Continuous monitoring, hospital, vital signs, ward patients, wearable sensors},
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keywords = {Continuous monitoring, hospital, patient deterioration, vital signs, ward patients, wearable sensors},
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}
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@article{downey_patient_2018,
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@ -224,7 +224,7 @@ Early warning score systems are widely used to facilitate detection of the deter
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urldate = {2023-04-26},
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date = {2018-06-01},
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langid = {english},
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keywords = {Vital signs, Monitoring, Early warning scores, Interviews, Patient experience},
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keywords = {Early warning scores, Interviews, Monitoring, Patient experience, Vital signs},
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file = {ScienceDirect Snapshot:/home/ulinja/Zotero/storage/BBCZQB5R/S1386505618302508.html:text/html;Submitted Version:/home/ulinja/Zotero/storage/AL4WYTXJ/Downey et al. - 2018 - Patient attitudes towards remote continuous vital .pdf:application/pdf},
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}
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@ -242,7 +242,7 @@ Early warning score systems are widely used to facilitate detection of the deter
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urldate = {2023-04-26},
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date = {2022-04-01},
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langid = {english},
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keywords = {Vital signs, Clinical alarms, Clinical deterioration, Physiological monitoring, Telemonitoring},
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keywords = {Clinical alarms, Clinical deterioration, Physiological monitoring, Telemonitoring, Vital signs},
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file = {Full Text PDF:/home/ulinja/Zotero/storage/V3VSFEIQ/van Rossum et al. - 2022 - Adaptive threshold-based alarm strategies for cont.pdf:application/pdf},
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}
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@ -262,7 +262,7 @@ Early warning score systems are widely used to facilitate detection of the deter
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pmid = {32212979},
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note = {Publisher: Taylor \& Francis
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\_eprint: https://doi.org/10.1080/13696998.2020.1747474},
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keywords = {continuous monitoring, vital signs, cost-effectiveness analysis, D70, H51, {SensiumVitals}, surgical patients},
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keywords = {continuous monitoring, cost-effectiveness analysis, D70, H51, {SensiumVitals}, surgical patients, vital signs},
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file = {Full Text PDF:/home/ulinja/Zotero/storage/ZZ7Q5R9K/Javanbakht et al. - 2020 - Cost utility analysis of continuous and intermitte.pdf:application/pdf},
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}
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@ -305,7 +305,7 @@ Antecedentes: Los pacientes con paro cardı́aco no esperado intrahospitalario t
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date = {2019-01-15},
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langid = {english},
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note = {Publisher: Public Library of Science},
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keywords = {Heart rate, Cardiac arrest, Blood pressure, Cohort studies, Medical risk factors, Oxygen, Respiration, Systematic reviews},
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keywords = {Blood pressure, Cardiac arrest, Cohort studies, Heart rate, Medical risk factors, Oxygen, Respiration, Systematic reviews},
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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},
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}
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@ -349,7 +349,7 @@ Antecedentes: Los pacientes con paro cardı́aco no esperado intrahospitalario t
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langid = {english},
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note = {Number: 22
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Publisher: Multidisciplinary Digital Publishing Institute},
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keywords = {early warning score, vital signs, {kNN}-{LS}-{SVM}, time-series prediction, wearable technology},
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keywords = {early warning score, {kNN}-{LS}-{SVM}, time-series prediction, vital signs, wearable technology},
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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},
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}
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@ -379,7 +379,7 @@ Publisher: Multidisciplinary Digital Publishing Institute},
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langid = {english},
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note = {Number: 9
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Publisher: Multidisciplinary Digital Publishing Institute},
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keywords = {{IoT}, {ECG}, edge computing, heartbeat detection, mobile healthcare, {QRS} detection, wearable device},
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keywords = {{ECG}, edge computing, heartbeat detection, {IoT}, mobile healthcare, {QRS} detection, wearable device},
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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},
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}
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@ -411,7 +411,7 @@ Publisher: Multidisciplinary Digital Publishing Institute},
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urldate = {2023-04-26},
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date = {2022-01-01},
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langid = {english},
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keywords = {Healthcare, Real-time monitoring, Abnormality detection, Alert notification, Internet of thing, Mobile communication, Personal service application, Vital sign monitoring},
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keywords = {Abnormality detection, Alert notification, Healthcare, Internet of thing, Mobile communication, Personal service application, Real-time monitoring, Vital sign monitoring},
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file = {Full Text PDF:/home/ulinja/Zotero/storage/XTBR4NVR/Sahu et al. - 2022 - Vital Sign Monitoring System for Healthcare Throug.pdf:application/pdf},
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}
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@ -429,7 +429,7 @@ Publisher: Multidisciplinary Digital Publishing Institute},
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urldate = {2023-04-26},
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date = {2022-10-01},
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langid = {english},
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keywords = {Remote patient monitoring, Abnormality detection, Internet of thing, Alert Notification, Mobile Communication},
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keywords = {Abnormality detection, Alert Notification, Internet of thing, Mobile Communication, Remote patient monitoring},
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file = {Full Text PDF:/home/ulinja/Zotero/storage/BUVVMQQ9/Sahu et al. - 2022 - Cloud-Based Remote Patient Monitoring System with .pdf:application/pdf},
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}
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@ -493,7 +493,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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urldate = {2023-04-26},
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date = {2018-11-01},
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langid = {english},
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keywords = {Early warning score, Critical care, Deteriorating patients, Pre hospital setting, Track and trigger system},
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keywords = {Critical care, Deteriorating patients, Early warning score, Pre hospital setting, Track and trigger system},
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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},
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}
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@ -510,7 +510,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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urldate = {2023-04-26},
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date = {2022-05-01},
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langid = {english},
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keywords = {{IoT}, {COVID}-19, Architecture, Consent, {NEWS}-2, Remote monitoring},
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keywords = {Architecture, Consent, {COVID}-19, {IoT}, {NEWS}-2, Remote monitoring},
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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},
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}
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@ -525,7 +525,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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author = {Tiwari, Divyanshu and Prasad, Devendra and Guleria, Kalpna and Ghosh, Pinaki},
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date = {2021-10},
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note = {{ISSN}: 2643-8615},
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keywords = {{IoT}, Medical services, Monitoring, Remote monitoring, Biomedical monitoring, Costs, Health monitoring, healthcare, heart monitoring devices, medical services, Signal processing},
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keywords = {Biomedical monitoring, Costs, Health monitoring, healthcare, heart monitoring devices, {IoT}, medical services, Medical services, Monitoring, Remote monitoring, Signal processing},
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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},
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}
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@ -556,7 +556,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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editor = {R, Shriram and Sharma, Mak},
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date = {2018},
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langid = {english},
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keywords = {{IoT}, Health monitoring, Diverse emergency situation, Tele-medicine},
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keywords = {Diverse emergency situation, Health monitoring, {IoT}, Tele-medicine},
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}
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@inproceedings{karvounis_hospital_2021,
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@ -568,7 +568,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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booktitle = {2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference ({SEEDA}-{CECNSM})},
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author = {Karvounis, Evaggelos and Vavva, Maria and Giannakeas, Nikolaos and Tzallas, Alexandros T. and Smanis, Ioannis and Tsipouras, Markos G.},
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date = {2021-09},
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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},
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keywords = {Artificial Intelligence ({AI}), component, Electronic healthcare, health monitoring, Hospitals, Internet of Things, Sensor systems, Smart healthcare, Temperature measurement, Temperature sensors, ubiquitous computing, wearable devices, Wireless communication, Wireless Sensor Network, Wireless sensor networks},
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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},
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}
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@ -607,7 +607,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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date = {2022-08-15},
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note = {Publisher: Taylor \& Francis
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\_eprint: https://doi.org/10.1080/03772063.2022.2110528},
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keywords = {Internet of things, Early warning score, Automated {EWS}, In-home system, Physiological parameters, Sensors},
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keywords = {Automated {EWS}, Early warning score, In-home system, Internet of things, Physiological parameters, Sensors},
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file = {Full Text PDF:/home/ulinja/Zotero/storage/2JFXM2RX/Sahu et al. - 2022 - Internet-of-Things-Enabled Early Warning Score Sys.pdf:application/pdf},
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}
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@ -627,7 +627,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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date = {2023},
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langid = {english},
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note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/widm.1485},
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keywords = {{IoT}, artificial intelligence, noninvasive technology, remote patient monitoring},
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keywords = {artificial intelligence, {IoT}, noninvasive technology, remote patient monitoring},
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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},
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}
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@ -640,7 +640,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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booktitle = {2021 International Conference on Technological Advancements and Innovations ({ICTAI})},
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author = {Quraishi, Suhail Javed and Yusuf, Humra},
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date = {2021-11},
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keywords = {Healthcare, Internet of Things, {IoT}, Medical services, Sensors, Bibliographies, Information technologies, Inspection, Real-time systems, Remote inspection, Smart devices, Technological innovation},
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keywords = {Bibliographies, Healthcare, Information technologies, Inspection, Internet of Things, {IoT}, Medical services, Real-time systems, Remote inspection, Sensors, Smart devices, Technological innovation},
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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},
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}
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@ -653,7 +653,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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booktitle = {2022 International Conference on Advanced Computing Technologies and Applications ({ICACTA})},
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author = {B V, Santhosh Krishna and Sharma, Sanjeev and Swathi, Kurapati Sai and Yamini, Korapati Reddy and Kiran, Chokkam Preethi and Chandrika, Kamineni},
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date = {2022-03},
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keywords = {Internet of Things, Security, Medical services, Monitoring, Diagnosis, Electrocardiography, Encryption, Heart, Internet of Things [{IoT}], Perception, Productivity},
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keywords = {Diagnosis, Electrocardiography, Encryption, Heart, Internet of Things, Internet of Things [{IoT}], Medical services, Monitoring, Perception, Productivity, Security},
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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},
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}
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@ -666,7 +666,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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booktitle = {2020 International Symposium on Networks, Computers and Communications ({ISNCC})},
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author = {de Mello Dantas, Hugo and Miceli de Farias, Claudio},
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date = {2020-10},
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keywords = {Internet of Things, Medical services, Monitoring, Wireless communication, Biomedical monitoring, Data integration, Emergency Detection, Remote Health Monitoring, Uncertainty, Wireless Body Sensor Networks},
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keywords = {Biomedical monitoring, Data integration, Emergency Detection, Internet of Things, Medical services, Monitoring, Remote Health Monitoring, Uncertainty, Wireless Body Sensor Networks, Wireless communication},
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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},
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}
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@ -684,7 +684,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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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},
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date = {2016},
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langid = {english},
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keywords = {Remote patient monitoring, Early warning score, Internet-of-Things, e-Health},
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keywords = {e-Health, Early warning score, Internet-of-Things, Remote patient monitoring},
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}
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@article{gomez_patient_2016,
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@ -701,7 +701,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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urldate = {2023-04-26},
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date = {2016-01-01},
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langid = {english},
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keywords = {Internet of Things, E-Health, Context Awareness, Ontology},
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keywords = {Context Awareness, E-Health, Internet of Things, Ontology},
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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},
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}
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@ -714,7 +714,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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booktitle = {2016 17th International Carpathian Control Conference ({ICCC})},
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author = {Archip, Alexandru and Botezatu, Nicolae and Şerban, Elena and Herghelegiu, Paul-Corneliu and Zală, Andrei},
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date = {2016-05},
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keywords = {Internet of Things, Remote patient monitoring, Internet of things, Monitoring, Temperature sensors, Prototypes, Biomedical monitoring, Electrocardiography, E-health, Embedded Systems, Logic gates, {RESTful} Web Services},
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keywords = {Biomedical monitoring, E-health, Electrocardiography, Embedded Systems, Internet of things, Internet of Things, Logic gates, Monitoring, Prototypes, Remote patient monitoring, {RESTful} Web Services, Temperature sensors},
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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},
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}
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@ -727,7 +727,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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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})},
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author = {Chowdary, Kovuru Chandu and Lokesh Krishna, K. and Prasad, K Lalu and Thejesh, K.},
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date = {2018-08},
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keywords = {Medical services, Monitoring, Temperature measurement, Temperature sensors, Blood pressure, Remote monitoring, Biomedical monitoring, and {IoT}, Blood flow rate, {GSM}, Microcontroller, temperature Sensor node},
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keywords = {and {IoT}, Biomedical monitoring, Blood flow rate, Blood pressure, {GSM}, Medical services, Microcontroller, Monitoring, Remote monitoring, Temperature measurement, temperature Sensor node, Temperature sensors},
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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},
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}
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@ -741,14 +741,16 @@ Very low and high {EWS} are able to discriminate between patients who are not li
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author = {Athira, A. and Devika, T.D. and Varsha, K.R. and Bose S., Sree Sanjanaa},
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date = {2020-03},
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note = {{ISSN}: 2575-7288},
|
||||
keywords = {Heart rate, Medical services, Monitoring, Temperature measurement, Temperature sensors, Biomedical monitoring, {IOT}, {MPM}, Smart Health, {SVM} classifier},
|
||||
keywords = {Biomedical monitoring, Heart rate, {IOT}, Medical services, Monitoring, {MPM}, Smart Health, {SVM} classifier, Temperature measurement, Temperature sensors},
|
||||
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},
|
||||
}
|
||||
|
||||
@online{noauthor_features_nodate,
|
||||
@online{zarabzadeh_features_2012-1,
|
||||
title = {Features of electronic Early Warning systems which impact clinical decision making {\textbar} {IEEE} Conference Publication {\textbar} {IEEE} Xplore},
|
||||
url = {https://ieeexplore.ieee.org/document/6266394},
|
||||
author = {Zarabzadeh, Atieh},
|
||||
urldate = {2023-04-26},
|
||||
date = {2012},
|
||||
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},
|
||||
}
|
||||
|
||||
@ -776,7 +778,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
|
||||
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},
|
||||
date = {2016-10},
|
||||
keywords = {Internet of Things, Cloud computing, Biomedical monitoring, Electrocardiography, Logic gates, Autonomic computing, Computer architecture, Fog Comouting, machine learning, Patient monitoring},
|
||||
keywords = {Autonomic computing, Biomedical monitoring, Cloud computing, Computer architecture, Electrocardiography, Fog Comouting, Internet of Things, Logic gates, machine learning, Patient monitoring},
|
||||
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},
|
||||
}
|
||||
|
||||
@ -789,7 +791,7 @@ Very low and high {EWS} are able to discriminate between patients who are not li
|
||||
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, Medical services, Monitoring, Temperature measurement, Temperature sensors, Biomedical monitoring, health care system, internet of things, Kinect, sensors, smart environment},
|
||||
keywords = {Biomedical monitoring, health care system, internet of things, Internet of Things, Kinect, Medical services, Monitoring, sensors, smart environment, Temperature measurement, Temperature sensors},
|
||||
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},
|
||||
}
|
||||
|
||||
@ -855,7 +857,7 @@ The {NEWS} measured at different time points was a good predictor of patient out
|
||||
urldate = {2023-04-27},
|
||||
date = {2015-05-01},
|
||||
langid = {english},
|
||||
keywords = {Monitoring, Early warning score, Deteriorating patients, Physiological parameters, Clinical outcomes, {NEWS}},
|
||||
keywords = {Clinical outcomes, Deteriorating patients, Early warning score, Monitoring, {NEWS}, Physiological parameters},
|
||||
file = {ScienceDirect Snapshot:/home/ulinja/Zotero/storage/HI4XZEPG/S0300957215000787.html:text/html},
|
||||
}
|
||||
|
||||
@ -895,7 +897,7 @@ Publisher: {BioMed} Central},
|
||||
pmid = {32584161},
|
||||
note = {Publisher: Taylor \& Francis
|
||||
\_eprint: https://doi.org/10.1080/23744235.2020.1784457},
|
||||
keywords = {{ICU}, {COVID}-19, National Early Warning Score 2, {NEWS}2, {SARS}-{CoV}-2},
|
||||
keywords = {{COVID}-19, {ICU}, National Early Warning Score 2, {NEWS}2, {SARS}-{CoV}-2},
|
||||
}
|
||||
|
||||
@article{otoom_iot-based_2020,
|
||||
@ -911,7 +913,7 @@ Publisher: {BioMed} Central},
|
||||
urldate = {2023-04-27},
|
||||
date = {2020-09-01},
|
||||
langid = {english},
|
||||
keywords = {Internet of Things, Real-time monitoring, {COVID}-19, Coronaviruses, Early identification or prediction, Treatment response},
|
||||
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},
|
||||
}
|
||||
|
||||
@ -926,7 +928,7 @@ Publisher: {BioMed} Central},
|
||||
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 = {Healthcare, Internet of Things, Cloud computing, Medical services, Monitoring, {COVID}-19, Sensors, Biomedical monitoring, platform, Protocols, remote monitoring},
|
||||
keywords = {Biomedical monitoring, Cloud computing, {COVID}-19, Healthcare, Internet of Things, 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},
|
||||
}
|
||||
|
||||
@ -945,7 +947,7 @@ Publisher: {BioMed} Central},
|
||||
urldate = {2023-04-27},
|
||||
date = {2021-01-21},
|
||||
langid = {english},
|
||||
keywords = {{COVID}-19, Blood parameters, {NEWS}2 score, Prediction model},
|
||||
keywords = {Blood parameters, {COVID}-19, {NEWS}2 score, Prediction model},
|
||||
file = {Full Text PDF:/home/ulinja/Zotero/storage/4RTVXPRT/Carr et al. - 2021 - Evaluation and improvement of the National Early W.pdf:application/pdf},
|
||||
}
|
||||
|
||||
@ -1026,7 +1028,7 @@ Publisher: {BioMed} Central},
|
||||
urldate = {2023-04-28},
|
||||
date = {2019-06-01},
|
||||
langid = {english},
|
||||
keywords = {Early warning score, Clinical research, Early mortality, Prehospital care, Prognosis},
|
||||
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},
|
||||
}
|
||||
|
||||
@ -1051,7 +1053,7 @@ Pre-hospital {NEWS} was associated with death or critical care unit escalation w
|
||||
urldate = {2023-04-28},
|
||||
date = {2018-03-01},
|
||||
langid = {english},
|
||||
keywords = {Clinical research, Acute care emergency ambulance systems, Intensive care, Pre-hospital},
|
||||
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},
|
||||
}
|
||||
|
||||
|
||||
@ -31,12 +31,17 @@ I have used the following search queries:
|
||||
|
||||
```text
|
||||
TITLE-ABS-KEY (("patient" OR "clinical" OR "medical") AND ("deterioration" OR "instability" OR "decompensation" OR "admission" OR "hospitalization" OR "escalation" OR "triage" OR "emergency"))
|
||||
AND TITLE-ABS-KEY ( "early warning" OR "early warning score" OR "warning" OR "score*" OR "EWS" )
|
||||
AND TITLE-ABS-KEY ( "early warning" OR "early warning score" OR "warning" OR "score*" OR "*EWS" )
|
||||
AND TITLE-ABS-KEY ( "system*" OR "automat*" OR "smart*" OR "wearable*" OR "internet of thing*" OR "IOT" OR "digital" OR "sensor*" OR "signal" OR "intelligen*" OR "predict*" OR "monitor*" OR "sreen*" OR ( ( "vital*" OR "bio*" ) AND ( "marker*" OR "sign*" OR "monitor*" ) ))
|
||||
AND TITLE-ABS-KEY ( 'continuous' OR 'outpatient' OR 'home' OR 'remote' OR 'domestic')
|
||||
|
||||
|
||||
TITLE-ABS-KEY (("patient" OR "clinical" OR "medical") AND ("deterioration" OR "instability" OR "decompensation" OR "admission" OR "hospitalization" OR "escalation" OR "triage" OR "emergency"))
|
||||
AND TITLE-ABS-KEY ( "early warning" OR "early warning score" OR "warning" OR "score*" OR "EWS" OR "early warning system" OR "rapid response system" )
|
||||
AND TITLE-ABS-KEY ( "Avoidance" OR "Prophylaxis" OR "Preclusion" OR "Anticipation" OR "Hindrance" OR "Obviation" OR "Deterrence" OR "Preemption" OR "Abstention" OR "Restraint" OR "Inhibition" OR "Exclusion" OR "Repression" OR "Suppression" )
|
||||
AND TITLE-ABS-KEY ( "avoidance" OR "prophylaxis" OR "preclusion" OR "anticipation" OR "hindrance" OR "obviation" OR "deterrence" OR "preemption" OR "abstention" OR "restraint" OR "inhibition" OR "exclusion" OR "repression" OR "suppression" )
|
||||
|
||||
|
||||
TITLE-ABS-KEY( ( ("patient" OR "clinical" OR "medical") AND ("deterioration" OR "instability" OR "decompensation" OR "admission" OR "hospitalization" OR "escalation" OR "triage" OR "emergency") ) OR ( "early warning" OR "early warning score" OR "warning" OR "score*" OR "*EWS" ) ) AND TITLE-ABS-KEY ( ( "system*" OR "automat*" OR "smart*" OR "wearable*" OR "internet of thing*" OR "iot" OR "digital" OR "sensor*" OR "signal" OR "intelligen*" OR "predict*" OR "monitor*" OR "sreen*" OR "remote" OR "it" OR "comput*" OR "mobile" OR "5G" OR "network" (("vital*" OR "bio*") AND ("marker*" OR "sign*" OR "monitor*")) ) OR ( "home" OR "domestic" OR "community" OR "remote" OR "long*term" OR "nursing" OR "rehabilitation" OR "out*of*hospital" OR "telemedicine" OR "ehealth" OR "mhealth" ) )
|
||||
|
||||
|
||||
```
|
||||
|
||||
1
docs/proposal/figures/prisma-flowchart.drawio
Normal file
1
docs/proposal/figures/prisma-flowchart.drawio
Normal file
@ -0,0 +1 @@
|
||||
<mxfile host="Electron" modified="2023-06-04T14:28:46.178Z" agent="5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) draw.io/20.7.4 Chrome/106.0.5249.199 Electron/21.3.3 Safari/537.36" version="20.7.4" etag="F7tFCP4Nm3QTIsRK-8Ri" type="device"><diagram id="Nfhfv2VjyWhg4Jekitvn" name="Page-1">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</diagram></mxfile>
|
||||
BIN
docs/proposal/figures/prisma-flowchart.png
Normal file
BIN
docs/proposal/figures/prisma-flowchart.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 247 KiB |
@ -17,8 +17,34 @@
|
||||
%\usepackage[table]{xcolor}
|
||||
\usepackage{color}
|
||||
\usepackage[colorlinks]{hyperref}
|
||||
\usepackage{tcolorbox}
|
||||
\tcbuselibrary{most}
|
||||
\pagestyle{plain}
|
||||
|
||||
% Code listing
|
||||
\usepackage{listings}
|
||||
\definecolor{bgtinted}{HTML}{efefef}
|
||||
\definecolor{codegray}{HTML}{111111}
|
||||
\definecolor{codeorange}{HTML}{91632C}
|
||||
\definecolor{codegreen}{HTML}{3D5232}
|
||||
\definecolor{codepurple}{HTML}{4E3A52}
|
||||
\lstdefinestyle{mystyle}{
|
||||
backgroundcolor=\color{black!5!white},
|
||||
commentstyle=\color{codepurple},
|
||||
keywordstyle=\color{codegray},
|
||||
stringstyle=\color{codegreen},
|
||||
basicstyle=\ttfamily\scriptsize\color{codegray},
|
||||
breakatwhitespace=true,
|
||||
breaklines=true,
|
||||
captionpos=b,
|
||||
keepspaces=true,
|
||||
showspaces=false,
|
||||
showstringspaces=false,
|
||||
showtabs=false,
|
||||
tabsize=2
|
||||
}
|
||||
\lstset{style=mystyle}
|
||||
|
||||
% Citations
|
||||
%\usepackage{cite}
|
||||
\usepackage[backend=biber, style=vancouver]{biblatex}
|
||||
@ -75,7 +101,7 @@ The individual scores are then added together to produce the final MEWS.
|
||||
|
||||
Traditionally, doctors and nursing staff perform collection and evaluation of the data manually, often inputting data into an EWS-calculator by hand.
|
||||
However, as Eisenkraft et al. mentioned in 2023, ``some known setbacks of the NEWS and other scales are the frequency of scoring and
|
||||
response, integration into practice, a miscalculation by healthcare providers [...]''\cite{eisenkraft_developing_2023}{(p.2)}.
|
||||
response, integration into practice, and miscalculation by healthcare providers [...]''\cite{eisenkraft_developing_2023}{(p.2)}.
|
||||
|
||||
Remote patient monitoring (RPM) can improve deterioration detection\cite{shaik_remote_2023} by greatly reducing the
|
||||
amount of human interaction required to take measurements and perform EWS calculations.
|
||||
@ -88,6 +114,314 @@ commercially available\cite{noauthor_visi_nodate, noauthor_equivital_nodate, noa
|
||||
|
||||
%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.
|
||||
|
||||
|
||||
\section{Review of existing literature}
|
||||
|
||||
In order to examine the current state of scientific knowledge about the use of wearable devices for automated EWS monitoring of
|
||||
patients at home, a comprehensive review of the existing literature was conducted.
|
||||
By systematically examining and synthesizing the current body of knowledge, this review identified a variety of approaches for
|
||||
utilizing smart medical devices in post-discharge patient care, as well as existing limitations and challenges in future research
|
||||
in this rapidly evolving field.
|
||||
|
||||
\subsection{Search strategy}
|
||||
|
||||
A systematic search strategy was implemented on the Scopus database, aimed to encompass a broad spectrum of literature relevant
|
||||
to the use of smart medical devices for automated early warning score monitoring of patients dismissed from ambulant or hospital care.
|
||||
The search focused on topics related to the research area, encompassing the examination of EWS, hospital admission, care escalation,
|
||||
and medical emergencies in combination with IT automation, medical wearables and Internet of Things (IoT).
|
||||
The Scopus database was chosen for its extensive coverage of scholarly literature across multiple disciplines.
|
||||
|
||||
For the search strategy, the following inclusion and exclusion criteria were employed to select relevant articles:
|
||||
|
||||
Inclusion criteria:
|
||||
|
||||
\begin{itemize}
|
||||
\item Articles focusing on the utilization of medical wearable devices for remote patient monitoring
|
||||
\item Articles addressing the automated calculation of early warning scores
|
||||
\item Articles discussing the application of early warning scores outside of medical care facilities
|
||||
\end{itemize}
|
||||
|
||||
Exclusion criteria:
|
||||
|
||||
\begin{itemize}
|
||||
\item Non-English language articles
|
||||
\item Publications for which full-text access was not available
|
||||
\item Duplicate articles
|
||||
\item Articles outside of the \enquote{Computer Science} subject area
|
||||
\end{itemize}
|
||||
|
||||
The following Scopus query was used to identify relevant literature:
|
||||
|
||||
\begin{tcolorbox}[enhanced, center, width=0.95\linewidth, rounded corners=all, colframe=black!75!white, boxrule=0.5pt, colback=black!5!white]
|
||||
\begin{lstlisting}[language=SQL]
|
||||
TITLE-ABS-KEY(("patient" OR "clinical" OR "medical") AND ("deterioration" OR "instability" OR "decompensation" OR "admission" OR "hospitalization" OR "escalation" OR "triage" OR "emergency")) OR ("early warning" OR "early warning score" OR "warning" OR "score*" OR "EWS") AND TITLE-ABS-KEY("system" OR "automat*" OR "smart*" OR "wearable*" OR "internet of thing*" OR "iot" OR "digital" OR "sensor*" OR "signal" OR "intelligen*" OR "predict*" OR "monitor*" OR "sreen*" OR "remote" OR "it" OR "comput*" OR "mobile" OR "5G" OR "network" (("vital*" OR "bio*") AND ("marker*" OR "sign*" OR "monitor*"))) AND TITLE-ABS-KEY("home" OR "domestic" OR "community" OR "remote" OR "longterm" OR "nursing" OR "rehabilitation" OR "outof*hospital" OR "telemedicine" OR "ehealth" OR "mhealth")
|
||||
\end{lstlisting}
|
||||
\end{tcolorbox}
|
||||
|
||||
\subsection{Results}
|
||||
|
||||
\begin{figure}[h]
|
||||
\begin{center}
|
||||
\includegraphics[width=.5\textwidth]{./figures/prisma-flowchart.png}
|
||||
\caption{\label{prisma-flowchart}PRISMA flowchart showing screening and assessment of identified literature}
|
||||
\end{center}
|
||||
\end{figure}
|
||||
|
||||
An initial query on Scopus yielded a total of $N=1997$ records.
|
||||
After removing duplicates, $N=952$ records were excluded, resulting in $N=1045$ unique records.
|
||||
Upon screening the titles and abstracts, $N=963$ records did not meet the inclusion criteria, leaving $N=82$ articles to be assessed for
|
||||
eligibility in full text.
|
||||
Finally, after a thorough evaluation, $N=45$ articles were included for the literature review, providing insight into the current state of
|
||||
research on the use of smart medical devices for automated early warning score monitoring in patients transitioning from ambulant or
|
||||
hospital care.
|
||||
Figure \ref{prisma-flowchart} shows the literature assessment process.
|
||||
The list of reviewed literature is shown in Table \ref{inclusion-table}.
|
||||
|
||||
\begin{table}[!h]
|
||||
\adjustbox{max width=\textwidth}{
|
||||
\begin{NiceTabular}{rll}[hvlines,colortbl-like]
|
||||
\hline
|
||||
\textbf{Number} & \textbf{Title} & \textbf{Author(s), Year} \\
|
||||
\hline
|
||||
1 &
|
||||
Internet of things enabled in-home health monitoring system using early warning score\cite{anzanpour_internet_2015} &
|
||||
Anzanpour 2015 \\
|
||||
\hline
|
||||
2 &
|
||||
Context-Aware Early Warning System for In-Home Healthcare Using Internet-of-Things\cite{anzanpour_context-aware_2016} &
|
||||
Anzanpour 2016 \\
|
||||
\hline
|
||||
3 &
|
||||
An IoT based system for remote patient monitoring\cite{archip_iot_2016} &
|
||||
Archip 2016 \\
|
||||
\hline
|
||||
4 &
|
||||
Wireless sensor network-based smart room system for healthcare monitoring\cite{arnil_wireless_2011} &
|
||||
Arnil 2011 \\
|
||||
\hline
|
||||
5 &
|
||||
Design and Development of IOT Based Multi-Parameter Patient Monitoring System\cite{athira_design_2020} &
|
||||
Athira 2020 \\
|
||||
\hline
|
||||
6 &
|
||||
Medical warning system based on Internet of Things using fog computing\cite{azimi_medical_2016} &
|
||||
Azimi 2016 \\
|
||||
\hline
|
||||
7 &
|
||||
Self-aware early warning score system for IoT-based personalized healthcare\cite{azimi_self-aware_2017} &
|
||||
Azimi 2017 \\
|
||||
\hline
|
||||
8 &
|
||||
Review on IoT based Healthcare systems\cite{b_v_review_2022} &
|
||||
Krishna 2022 \\
|
||||
\hline
|
||||
9 &
|
||||
Effectiveness of Early Warning Scores for Early Severity Assessment in Outpatient Emergency Care: A Systematic Review\cite{burgos-esteban_effectiveness_2022} &
|
||||
Burgos-Esteban 2022 \\
|
||||
\hline
|
||||
10 &
|
||||
A QRS Detection and R Point Recognition Method for Wearable Single-Lead ECG Devices\cite{chen_qrs_2017} &
|
||||
Chen 2017 \\
|
||||
\hline
|
||||
11 &
|
||||
Adopting the Internet of Things technologies in health care systems\cite{chiuchisan_adopting_2014} &
|
||||
Chiuchisan 2014 \\
|
||||
\hline
|
||||
12 &
|
||||
An Efficient Wireless Health Monitoring System\cite{chowdary_efficient_2018} &
|
||||
Chowdary 2018 \\
|
||||
\hline
|
||||
13 &
|
||||
DeepSigns: A predictive model based on Deep Learning for the early detection of patient health deterioration\cite{da_silva_deepsigns_2021} &
|
||||
da Silva 2021 \\
|
||||
\hline
|
||||
14 &
|
||||
Use of ultra-low cost fitness trackers as clinical monitors in low resource emergency departments\cite{dagan_use_2020} &
|
||||
Dagan 2020 \\
|
||||
\hline
|
||||
15 &
|
||||
A data fusion algorithm for clinically relevant anomaly detection in remote health monitoring\cite{de_mello_dantas_data_2020} &
|
||||
de Mello Dantas 2020 \\
|
||||
\hline
|
||||
16 &
|
||||
Patient attitudes towards remote continuous vital signs monitoring on general surgery wards: An interview study\cite{downey_strengths_2017} &
|
||||
Downey 2018 \\
|
||||
\hline
|
||||
17 &
|
||||
Developing a real-time detection tool and an early warning score using a continuous wearable multi-parameter monitor\cite{eisenkraft_developing_2023} &
|
||||
Eisenkraft 2023 \\
|
||||
\hline
|
||||
18 &
|
||||
An IoT-Based Healthcare Platform for Patients in ICU Beds During the COVID-19 Outbreak\cite{filho_iot-based_2021} &
|
||||
Filho 2021 \\
|
||||
\hline
|
||||
19 &
|
||||
Patient Monitoring System Based on Internet of Things\cite{gomez_patient_2016} &
|
||||
Gomez 2016 \\
|
||||
\hline
|
||||
20 &
|
||||
Continuous monitoring is superior to manual measurements in detecting vital sign deviations in patients with COVID-19\cite{gronbaek_continuous_2023} &
|
||||
Gronbaek 2023 \\
|
||||
\hline
|
||||
21 &
|
||||
Secure and lightweight privacy preserving Internet of things integration for remote patient monitoring\cite{imtyaz_ahmed_secure_2022} &
|
||||
Imtyaz 2022 \\
|
||||
\hline
|
||||
22 &
|
||||
Remote Continuous Health Monitoring System for Patients\cite{jagadish_remote_2018} &
|
||||
Jagadish 2018 \\
|
||||
\hline
|
||||
23 &
|
||||
Cost utility analysis of continuous and intermittent versus intermittent vital signs monitoring in patients admitted to surgical wards\cite{javanbakht_cost_2020} &
|
||||
Javanbakht 2020 \\
|
||||
\hline
|
||||
24 &
|
||||
Wearable sensors to improve detection of patient deterioration\cite{joshi_wearable_2019} &
|
||||
Joshi 2019 \\
|
||||
\hline
|
||||
25 &
|
||||
Intelligent Healthcare\cite{kale_intelligent_2021} &
|
||||
Kale 2021 \\
|
||||
\hline
|
||||
26 &
|
||||
A Hospital Healthcare Monitoring System Using Internet of Things Technologies\cite{karvounis_hospital_2021} &
|
||||
Karvounis 2021 \\
|
||||
\hline
|
||||
27 &
|
||||
All-day mobile healthcare monitoring system based on heterogeneous stretchable sensors for medical emergency\cite{lee_all-day_2020} &
|
||||
Lee 2020 \\
|
||||
\hline
|
||||
28 &
|
||||
Analysis of the early warning score to detect critical or high-risk patients in the prehospital setting\cite{martin-rodriguez_analysis_2019} &
|
||||
Martin-Rodriguez 2019 \\
|
||||
\hline
|
||||
29 &
|
||||
An IoT-based framework for early identification and monitoring of COVID-19 cases\cite{otoom_iot-based_2020} &
|
||||
Otoom 2020 \\
|
||||
\hline
|
||||
30 &
|
||||
A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home\cite{paganelli_conceptual_2022} &
|
||||
Paganelli 2022 \\
|
||||
\hline
|
||||
31 &
|
||||
Personalized Mobile Health for Elderly Home Care: A Systematic Review of Benefits and Challenges\cite{pahlevanynejad_personalized_2023} &
|
||||
Pahlevanynejad 2023 \\
|
||||
\hline
|
||||
32 &
|
||||
CuraBand: Health Monitoring and Warning System\cite{phaltankar_curaband_2021} &
|
||||
Phaltankar 2021 \\
|
||||
\hline
|
||||
33 &
|
||||
Internet of Things in Healthcare, A Literature Review\cite{quraishi_internet_2021} &
|
||||
Quraishi 2021 \\
|
||||
\hline
|
||||
34 &
|
||||
Vital Sign Monitoring System for Healthcare Through IoT Based Personal Service Application\cite{sahu_vital_2022} &
|
||||
Sahu 2022 \\
|
||||
\hline
|
||||
35 &
|
||||
Internet-of-Things-Enabled Early Warning Score System for Patient Monitoring\cite{sahu_internet--things-enabled_2022} &
|
||||
Sahu 2022 \\
|
||||
\hline
|
||||
36 &
|
||||
Cloud-Based Remote Patient Monitoring System with Abnormality Detection and Alert Notification\cite{sahu_cloud-based_2022} &
|
||||
Sahu 2022 \\
|
||||
\hline
|
||||
37 &
|
||||
Remote patient monitoring using artificial intelligence: Current state, applications, and challenges\cite{shaik_remote_2023} &
|
||||
Shaik 2023 \\
|
||||
\hline
|
||||
38 &
|
||||
Prototype development of continuous remote monitoring of ICU patients at home\cite{thippeswamy_prototype_2021} &
|
||||
Thippeswamy 2021 \\
|
||||
\hline
|
||||
39 &
|
||||
IoT based Smart Healthcare Monitoring Systems: A Review\cite{tiwari_iot_2021} &
|
||||
Tiwari 2021 \\
|
||||
\hline
|
||||
40 &
|
||||
Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients\cite{un_observational_2021} &
|
||||
Un 2021 \\
|
||||
\hline
|
||||
41 &
|
||||
Adaptive threshold-based alarm strategies for continuous vital signs monitoring\cite{van_rossum_adaptive_2022} &
|
||||
van Rossum 2022 \\
|
||||
\hline
|
||||
42 &
|
||||
A retrospective comparison of the Modified Early Warning Score (MEWS) and machine learning approach\cite{wu_predicting_2021} &
|
||||
Wu 2021 \\
|
||||
\hline
|
||||
43 &
|
||||
IoT based Real Time Health Monitoring\cite{yeri_iot_2020} &
|
||||
Yeri 2020 \\
|
||||
\hline
|
||||
44 &
|
||||
Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology\cite{youssef_ali_amer_vital_2020} &
|
||||
Youssef Ali Amer 2020 \\
|
||||
\hline
|
||||
45 &
|
||||
Features of electronic Early Warning systems which impact clinical decision making\cite{zarabzadeh_features_2012} &
|
||||
Zarabzadeh 2012 \\
|
||||
\hline
|
||||
\end{NiceTabular}
|
||||
}
|
||||
\caption{\label{inclusion-table}List of included articles}
|
||||
\end{table}
|
||||
|
||||
% TODO for all outcomes, present and compare the findings of each study
|
||||
|
||||
\subsection{Discussion}
|
||||
|
||||
While the application of EWS in ambulant care facilities and hospitals has been thoroughly investigated,
|
||||
very little research has been done to assess their practicability for remote monitoring of at-risk patients at home.
|
||||
Furthermore, it was observed that previous research on the use of IoT-devices for this purpose was largely conducted in
|
||||
experimental settings, limiting the generalizability of the results.
|
||||
Some studies have examined monitoring vital signs of at-home-patients for abnormalities,
|
||||
however in most of them, no automated EWS calculations were made\cite{archip_iot_2016, azimi_medical_2016, chowdary_efficient_2018, yeri_iot_2020, lee_all-day_2020, athira_design_2020, phaltankar_curaband_2021, thippeswamy_prototype_2021}.
|
||||
In 2015, Anzanpour et al. developed a monitoring system which collects vitals data and calculates EWS, however due to limited or nonexistent
|
||||
availability of wireless sensors for all relevant vital signs, the work was limited to using a laboratory prototype
|
||||
and required manual interaction in transferring vitals data\cite{anzanpour_internet_2015}.
|
||||
Sahu et al. documented their development of an EWS-supported digital early warning system using the PM6750\cite{sahu_internet--things-enabled_2022},
|
||||
an experimental vitals data monitoring device capable of taking continuous measurements in a laboratory setting\cite{noauthor_pm6750_nodate}.
|
||||
However, the methodology they used to calculate EWS in real-time with laboratory data is both inconsistent and weak.
|
||||
|
||||
Recent studies indicate a growing trend towards investigating automated EWS calculations in real-world scenarios\cite{downey_strengths_2017, karvounis_hospital_2021, b_v_review_2022, dagan_use_2020}.
|
||||
Notably, the availability of comprehensive, mobile vital signs monitoring equipment has seen a significant increase, especially in the wake of the COVID-19
|
||||
pandemic\cite{paganelli_conceptual_2022, filho_iot-based_2021, otoom_iot-based_2020, gronbaek_continuous_2023}.
|
||||
This surge in accessibility has paved the way for more extensive and continuous monitoring of patients in non-medical care settings.
|
||||
Moreover, there is a growing interest in incorporating machine learning algorithms to enhance the predictive capabilities of
|
||||
deterioration detection\cite{un_observational_2021, da_silva_deepsigns_2021, de_mello_dantas_data_2020}.
|
||||
This demonstrates the evolving landscape of remote patient monitoring, aiming to improve clinical outcomes and alleviate the
|
||||
burden on hospital wards.
|
||||
|
||||
Despite the wealth of literature reviewed, no existing empirical studies evaluating the use of early warning scores for
|
||||
patients at home were identified.
|
||||
This highlights a crucial research gap and prompts the need for further investigation in this area, potentially warranting the development
|
||||
of an EWS specialized for use outside of medical care facilities.
|
||||
|
||||
\subsection{Interpretation of Results}
|
||||
|
||||
Based on the findings, several key implications can be drawn.
|
||||
Firstly, the improved availability of smart sensors and the demonstrated effectiveness of EWS in predicting deterioration in direct
|
||||
medical care settings warrant research into their utilization at home.
|
||||
By remotely monitoring patients, it may be possible to identify early signs of deterioration, enabling earlier dismissal from
|
||||
hospital care and thereby freeing up valuable resources.
|
||||
Additionally, this approach holds the potential to reduce mortality rates and minimize the frequency of adverse clinical outcomes.
|
||||
|
||||
However, it is important to acknowledge the lack of research on the use of EWS at home, which calls for a feasibility study in this
|
||||
specific context.
|
||||
This study would need to address challenges such as the frequency of measurements required and the absence of immediate diagnosis
|
||||
from qualified medical staff.
|
||||
Overcoming these obstacles is essential to ensure the safety and efficacy of automated remote patient monitoring in home-based settings.
|
||||
|
||||
In conclusion, the literature review highlights the increasing interest in using smart medical devices and EWS for remote patient
|
||||
monitoring, particularly in real-world scenarios.
|
||||
The absence of studies evaluating the application of EWS in patients at home underscores the need for further investigation in this area.
|
||||
Conducting a feasibility study to explore the practicality and challenges of implementing EWS in home-based care would contribute
|
||||
significantly to the existing body of knowledge and help advance the field of automated early warning score monitoring in
|
||||
non-medical care settings.
|
||||
|
||||
|
||||
\section{Motivation}
|
||||
|
||||
% TODO EWS makes prediction value better than monitoring abnormalities in single vital signs
|
||||
@ -101,16 +435,6 @@ biometric sensors into one device, allowing for a much higher degree of patient
|
||||
scalability\cite{un_observational_2021}.
|
||||
Therefore, utilizing such devices for RPM is a suitable approach.
|
||||
|
||||
While the application of EWS in ambulant care facilities and hospitals has been thoroughly investigated, very little research has been done to
|
||||
assess their practicability for remote monitoring of at-risk patients at home.
|
||||
Some studies have examined monitoring vital signs of at-home-patients for abnormalities in an experimental setting,
|
||||
however in most of them, no automated EWS calculations were made\cite{archip_iot_2016, azimi_medical_2016, chowdary_efficient_2018, yeri_iot_2020, lee_all-day_2020, athira_design_2020, phaltankar_curaband_2021, thippeswamy_prototype_2021}.
|
||||
In 2015, Anzanpour et al. developed a monitoring system which collects vitals data and calculates EWS, however due to limited or nonexistent
|
||||
availability of wireless sensors for all relevant vital signs, the work was limited to using a laboratory prototype
|
||||
and required manual interaction in transferring vitals data\cite{anzanpour_internet_2015}.
|
||||
Sahu et al. documented their development of an EWS-supported digital early warning system using the PM6750\cite{sahu_internet--things-enabled_2022},
|
||||
an experimental vitals data monitoring device capable of taking continuous measurements in a laboratory setting\cite{noauthor_pm6750_nodate}.
|
||||
However, the methodology they used to calculate EWS in real-time with laboratory data is both inconsistent and weak.
|
||||
|
||||
In summary, with the current availability of wearable, networked biosensors and the validated effectiveness of EWS in medical facilities,
|
||||
combining both aspects presents an important and interesting research opportunity which could help reduce mortality and improve clinical
|
||||
@ -120,7 +444,6 @@ outcomes for patients at risk of deterioration, both in their homes and on the g
|
||||
%Taking continuous measurements is superior to measuring intermittently\cite{gronbaek_continuous_2023, shaik_remote_2023}.
|
||||
|
||||
|
||||
\newpage
|
||||
\section{Objectives}
|
||||
|
||||
The objective of this research is to explore the practical feasibility of using an existing, clinically validated EWS to remotely monitor
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user