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Real-time infection prediction with wearable physiological monitoring and AI to aid military workforce readiness during COVID-19.
Conroy, Bryan; Silva, Ikaro; Mehraei, Golbarg; Damiano, Robert; Gross, Brian; Salvati, Emmanuele; Feng, Ting; Schneider, Jeffrey; Olson, Niels; Rizzo, Anne G; Curtin, Catherine M; Frassica, Joseph; McFarlane, Daniel C.
  • Conroy B; Philips Research North America, Cambridge, MA, USA.
  • Silva I; Philips Research North America, Cambridge, MA, USA.
  • Mehraei G; Philips Research North America, Cambridge, MA, USA.
  • Damiano R; Philips Research North America, Cambridge, MA, USA.
  • Gross B; Philips Research North America, Cambridge, MA, USA.
  • Salvati E; Philips Research North America, Cambridge, MA, USA.
  • Feng T; Philips Research North America, Cambridge, MA, USA.
  • Schneider J; Defense Innovation Unit, Mountain View, CA, USA.
  • Olson N; Defense Innovation Unit, Mountain View, CA, USA.
  • Rizzo AG; The Guthrie Clinic, Sayre, PA, USA.
  • Curtin CM; Department of Surgery, Palo Alto Veteran Affairs Healthcare System, Palo Alto, CA, USA.
  • Frassica J; Philips Research North America, Cambridge, MA, USA.
  • McFarlane DC; Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Sci Rep ; 12(1): 3797, 2022 03 08.
Article in English | MEDLINE | ID: covidwho-1908239
ABSTRACT
Infectious threats, like the COVID-19 pandemic, hinder maintenance of a productive and healthy workforce. If subtle physiological changes precede overt illness, then proactive isolation and testing can reduce labor force impacts. This study hypothesized that an early infection warning service based on wearable physiological monitoring and predictive models created with machine learning could be developed and deployed. We developed a prototype tool, first deployed June 23, 2020, that delivered continuously updated scores of infection risk for SARS-CoV-2 through April 8, 2021. Data were acquired from 9381 United States Department of Defense (US DoD) personnel wearing Garmin and Oura devices, totaling 599,174 user-days of service and 201 million hours of data. There were 491 COVID-19 positive cases. A predictive algorithm identified infection before diagnostic testing with an AUC of 0.82. Barriers to implementation included adequate data capture (at least 48% data was needed) and delays in data transmission. We observe increased risk scores as early as 6 days prior to diagnostic testing (2.3 days average). This study showed feasibility of a real-time risk prediction score to minimize workforce impacts of infection.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Algorithms / COVID-19 / Monitoring, Physiologic Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-07764-6

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Algorithms / COVID-19 / Monitoring, Physiologic Type of study: Diagnostic study / Prognostic study Limits: Humans Language: English Journal: Sci Rep Year: 2022 Document Type: Article Affiliation country: S41598-022-07764-6