Lu, F., Yao, Z., Cleveland, H. et al. (2 more authors) (2018) Bayesian hierarchical vector autoregressive models for patient-level predictive modeling. PLoS ONE, 13 (12). e0208082. ISSN 1932-6203
Abstract
Predicting health outcomes from longitudinal health histories is of central importance to healthcare. Observational healthcare databases such as patient diary databases provide a rich resource for patient-level predictive modeling. In this paper, we propose a Bayesian hierarchical vector autoregressive (VAR) model to predict medical and psychological conditions using multivariate time series data. Compared to the existing patient-specific predictive VAR models, our model demonstrated higher accuracy in predicting future observations in terms of both point and interval estimates due to the pooling effect of the hierarchical model specification. In addition, by adopting an elastic-net prior, our model offers greater interpretability about the associations between variables of interest on both the population level and the patient level, as well as between-patient heterogeneity. We apply the model to two examples: 1) predicting substance use craving, negative affect and tobacco use among college students, and 2) predicting functional somatic symptoms and psychological discomforts.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Copyright, Publisher and Additional Information: | © 2018 Lu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. https://creativecommons.org/licenses/by/4.0/ |
Dates: |
|
Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > The Medical School (Sheffield) > Academic Unit of Medical Education (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 11 Mar 2019 12:42 |
Last Modified: | 14 Mar 2019 00:30 |
Published Version: | https://doi.org/10.1371/journal.pone.0208082 |
Status: | Published |
Publisher: | Public Library of Science (PLoS) |
Refereed: | Yes |
Identification Number: | 10.1371/journal.pone.0208082 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:141713 |