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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Autores principales: 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
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Publicado: Public Library of Science (PLoS) 2021
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Acceso en línea:https://doaj.org/article/3f635a2ba0d14b759832399e7ba01013
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spelling oai:doaj.org-article:3f635a2ba0d14b759832399e7ba010132021-12-02T19:58:02ZBayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions.1553-734X1553-735810.1371/journal.pcbi.1009303https://doaj.org/article/3f635a2ba0d14b759832399e7ba010132021-08-01T00:00:00Zhttps://doi.org/10.1371/journal.pcbi.1009303https://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The 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, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention's effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.Jason LiuDaniel J SpakowiczGarrett I AshRebecca HoydRohan AhluwaliaAndrew ZhangShaoke LouDonghoon LeeJing ZhangCarolyn PresleyAnn GreeneMatthew Stults-KolehmainenLaura M NallyJulien S BakerLisa M FucitoStuart A WeinzimerAndrew V PapachristosMark GersteinPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 17, Iss 8, p e1009303 (2021)
institution DOAJ
collection DOAJ
language EN
topic Biology (General)
QH301-705.5
spellingShingle Biology (General)
QH301-705.5
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
Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions.
description 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, leading to the goal of precise phenotypic correlates for genotypes. Even though treatments and interventions are becoming more specific and datasets more abundant, measuring the causal impact of health interventions requires careful considerations of complex covariate structures, as well as knowledge of the temporal and spatial properties of the data. Thus, interpreting biomedical sensor data needs to make use of specialized statistical models. Here, we show how the Bayesian structural time series framework, widely used in economics, can be applied to these data. This framework corrects for covariates to provide accurate assessments of the significance of interventions. Furthermore, it allows for a time-dependent confidence interval of impact, which is useful for considering individualized assessments of intervention efficacy. We provide a customized biomedical adaptor tool, MhealthCI, around a specific implementation of the Bayesian structural time series framework that uniformly processes, prepares, and registers diverse biomedical data. We apply the software implementation of MhealthCI to a structured set of examples in biomedicine to showcase the ability of the framework to evaluate interventions with varying levels of data richness and covariate complexity and also compare the performance to other models. Specifically, we show how the framework is able to evaluate an exercise intervention's effect on stabilizing blood glucose in a diabetes dataset. We also provide a future-anticipating illustration from a behavioral dataset showcasing how the framework integrates complex spatial covariates. Overall, we show the robustness of the Bayesian structural time series framework when applied to biomedical sensor data, highlighting its increasing value for current and future datasets.
format article
author 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
author_facet 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
author_sort Jason Liu
title Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions.
title_short Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions.
title_full Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions.
title_fullStr Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions.
title_full_unstemmed Bayesian structural time series for biomedical sensor data: A flexible modeling framework for evaluating interventions.
title_sort bayesian structural time series for biomedical sensor data: a flexible modeling framework for evaluating interventions.
publisher Public Library of Science (PLoS)
publishDate 2021
url https://doaj.org/article/3f635a2ba0d14b759832399e7ba01013
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