Intelligent risk prediction in public health using wearable device data.
Journal
NPJ digital medicine
ISSN: 2398-6352
Titre abrégé: NPJ Digit Med
Pays: England
ID NLM: 101731738
Informations de publication
Date de publication:
13 Oct 2022
13 Oct 2022
Historique:
received:
19
09
2022
accepted:
30
09
2022
entrez:
13
10
2022
pubmed:
14
10
2022
medline:
14
10
2022
Statut:
epublish
Résumé
The importance of infection risk prediction as a key public health measure has only been underscored by the COVID-19 pandemic. In a recent study, researchers use machine learning to develop an algorithm that predicts the risk of COVID-19 infection, by combining biometric data from wearable devices like Fitbit, with electronic symptom surveys. In doing so, they aim to increase the efficiency of test allocation when tracking disease spread in resource-limited settings. But the implications of technology that applies data from wearables stretch far beyond infection monitoring into healthcare delivery and research. The adoption and implementation of this type of technology will depend on regulation, impact on patient outcomes, and cost savings.
Identifiants
pubmed: 36229593
doi: 10.1038/s41746-022-00701-x
pii: 10.1038/s41746-022-00701-x
pmc: PMC9556285
doi:
Types de publication
Editorial
Langues
eng
Pagination
153Informations de copyright
© 2022. The Author(s).
Références
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