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10.1038/s41598-021-82771-7 http://scihub22266oqcxt.onion/10.1038/s41598-021-82771-7 33623096!7902655!33623096     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33623096&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Sci+Rep 2021 ; 11 (1): 4388 Nephropedia Template TP {{tp|p=33623096|t=2021. Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients |pdf=|usr=}}{{33623096}} gab.com Text Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33623096 #FOAvip Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 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. Twit Text FOAVip Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=33623096 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33623096 #FOAvip English Wikipedia <ref>{{Cite pmid|33623096}}</ref> Observational study on wearable biosensors and machine learning-based remote monitoring of COVID-19 patients #MMPMID33623096 Un KC; Wong CK; Lau YM; Lee JC; Tam FC; Lai WH; Lau YM; Chen H; Wibowo S; Zhang X; Yan M; Wu E; Chan SC; Lee SM; Chow A; Tong RC; Majmudar MD; Rajput KS; Hung IF; Siu CW Sci Rep 2021[Feb]; 11 (1): 4388 PMID33623096show ga Patients infected with SARS-CoV-2 may deteriorate rapidly and therefore continuous monitoring is necessary. We conducted an observational study involving patients with mild COVID-19 to explore the potentials of wearable biosensors and machine learning-based analysis of physiology parameters to detect clinical deterioration. Thirty-four patients (median age: 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. |*COVID-19[MESH] |*Machine Learning[MESH] |*Wearable Electronic Devices[MESH] |Adult[MESH] |Biosensing Techniques/*methods[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Observational Studies as Topic[MESH]Observational study on wearable biosensors and machine learning-based (epmc).pdf DeepDyve Pubget Overpricing