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Passive detection of COVID-19 with wearable sensors and explainable machine learning algorithms.
Gadaleta, Matteo; Radin, Jennifer M; Baca-Motes, Katie; Ramos, Edward; Kheterpal, Vik; Topol, Eric J; Steinhubl, Steven R; Quer, Giorgio.
Afiliación
  • Gadaleta M; Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA.
  • Radin JM; Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA.
  • Baca-Motes K; Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA.
  • Ramos E; Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA.
  • Kheterpal V; CareEvolution, 625N Main Street, Ann Arbor, MI, 48104, USA.
  • Topol EJ; CareEvolution, 625N Main Street, Ann Arbor, MI, 48104, USA.
  • Steinhubl SR; Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA.
  • Quer G; Scripps Research Translational Institute, 3344N Torrey Pines Ct Plaza Level, La Jolla, CA, 92037, USA.
NPJ Digit Med ; 4(1): 166, 2021 Dec 08.
Article en En | MEDLINE | ID: mdl-34880366
Individual smartwatch or fitness band sensor data in the setting of COVID-19 has shown promise to identify symptomatic and pre-symptomatic infection or the need for hospitalization, correlations between peripheral temperature and self-reported fever, and an association between changes in heart-rate-variability and infection. In our study, a total of 38,911 individuals (61% female, 15% over 65) have been enrolled between March 25, 2020 and April 3, 2021, with 1118 reported testing positive and 7032 negative for COVID-19 by nasopharyngeal PCR swab test. We propose an explainable gradient boosting prediction model based on decision trees for the detection of COVID-19 infection that can adapt to the absence of self-reported symptoms and to the available sensor data, and that can explain the importance of each feature and the post-test-behavior for the individuals. We tested it in a cohort of symptomatic individuals who exhibited an AUC of 0.83 [0.81-0.85], or AUC = 0.78 [0.75-0.80] when considering only data before the test date, outperforming state-of-the-art algorithm in these conditions. The analysis of all individuals (including asymptomatic and pre-symptomatic) when self-reported symptoms were excluded provided an AUC of 0.78 [0.76-0.79], or AUC of 0.70 [0.69-0.72] when considering only data before the test date. Extending the use of predictive algorithms for detection of COVID-19 infection based only on passively monitored data from any device, we showed that it is possible to scale up this platform and apply the algorithm in other settings where self-reported symptoms can not be collected.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: NPJ Digit Med Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies Idioma: En Revista: NPJ Digit Med Año: 2021 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Reino Unido