Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions.

Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions.

The development of mobile-health technology has the potential to revolutionize personalized medicine. Biomedical sensors (e.g., wearables) can assist with determining treatment plans for individuals, provide quantitative information to healthcare providers, and give objective measurements of health,...

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Bibliographic Details
Main Authors: Jason Liu, Daniel J Spakowicz, Garrett I Ash, Rebecca Hoyd, Rohan Ahluwalia, Andrew Zhang, Shaoke Lou, Donghoon Lee, Jing Zhang, Carolyn Presley, Ann Greene, Matthew Stults-Kolehmainen, Laura M Nally, Julien S Baker, Lisa M Fucito, Stuart A Weinzimer, Andrew V Papachristos, Mark Gerstein
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2021-08-01
Series:PLoS Computational Biology
Online Access:https://doi.org/10.1371/journal.pcbi.1009303

Internet

https://doi.org/10.1371/journal.pcbi.1009303

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