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

ABSTRACT: 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 ser

SUBMITTER: Liu J 

PROVIDER: S-EPMC8412351 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature