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Article: Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients

TitleObservational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients
Authors
Issue Date2021
PublisherNature Research: Fully open access journals. The Journal's web site is located at http://www.nature.com/srep/index.html
Citation
Scientific Reports, 2021, v. 11, p. article no. 4388 How to Cite?
AbstractPatients 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: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 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 < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 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.
Persistent Identifierhttp://hdl.handle.net/10722/308206
ISSN
2021 Impact Factor: 4.996
2020 SCImago Journal Rankings: 1.240
PubMed Central ID
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorUn, KC-
dc.contributor.authorWong, CK-
dc.contributor.authorLau, YM-
dc.contributor.authorLee, JCY-
dc.contributor.authorTam, FCC-
dc.contributor.authorLai, WH-
dc.contributor.authorLau, YM-
dc.contributor.authorChen, H-
dc.contributor.authorWibowo, S-
dc.contributor.authorZhang, X-
dc.contributor.authorYan, M-
dc.contributor.authorWu, E-
dc.contributor.authorChan, SC-
dc.contributor.authorLee, SM-
dc.contributor.authorChow, A-
dc.contributor.authorTong, RCF-
dc.contributor.authorMajmudar, MD-
dc.contributor.authorRajput, KS-
dc.contributor.authorHung, IFN-
dc.contributor.authorSiu, CW-
dc.date.accessioned2021-11-12T13:43:58Z-
dc.date.available2021-11-12T13:43:58Z-
dc.date.issued2021-
dc.identifier.citationScientific Reports, 2021, v. 11, p. article no. 4388-
dc.identifier.issn2045-2322-
dc.identifier.urihttp://hdl.handle.net/10722/308206-
dc.description.abstractPatients 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: 32 years; male: 52.9%) with mild COVID-19 from Queen Mary Hospital were recruited. The mean National Early Warning Score 2 (NEWS2) were 0.59 ± 0.7. 1231 manual measurement of physiology parameters were performed during hospital stay (median 15 days). Physiology parameters obtained from wearable biosensors correlated well with manual measurement including pulse rate (r = 0.96, p < 0.0001) and oxygen saturation (r = 0.87, p < 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 < 0.0001) and NEWS2 (r = 0.75, p < 0.001). BI was superior to NEWS2 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.-
dc.languageeng-
dc.publisherNature Research: Fully open access journals. The Journal's web site is located at http://www.nature.com/srep/index.html-
dc.relation.ispartofScientific Reports-
dc.rightsScientific Reports. Copyright © Nature Research: Fully open access journals.-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleObservational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients-
dc.typeArticle-
dc.identifier.emailHung, IFN: ivanhung@hkucc.hku.hk-
dc.identifier.emailSiu, CW: cwdsiu@hkucc.hku.hk-
dc.identifier.authorityHung, IFN=rp00508-
dc.identifier.authoritySiu, CW=rp00534-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1038/s41598-021-82771-7-
dc.identifier.pmid33623096-
dc.identifier.pmcidPMC7902655-
dc.identifier.scopuseid_2-s2.0-85101591727-
dc.identifier.hkuros329884-
dc.identifier.volume11-
dc.identifier.spagearticle no. 4388-
dc.identifier.epagearticle no. 4388-
dc.identifier.isiWOS:000626731000011-
dc.publisher.placeUnited Kingdom-

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