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Bayesian hierarchical vector autoregressive models for patient-level predictive modeling

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Listed:
  • Feihan Lu
  • Yao Zheng
  • Harrington Cleveland
  • Chris Burton
  • David Madigan

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.

Suggested Citation

  • Feihan Lu & Yao Zheng & Harrington Cleveland & Chris Burton & David Madigan, 2018. "Bayesian hierarchical vector autoregressive models for patient-level predictive modeling," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-27, December.
  • Handle: RePEc:plo:pone00:0208082
    DOI: 10.1371/journal.pone.0208082
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    1. Stephen F Weng & Jenna Reps & Joe Kai & Jonathan M Garibaldi & Nadeem Qureshi, 2017. "Can machine-learning improve cardiovascular risk prediction using routine clinical data?," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
    2. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    3. Gregor Stiglic & Petra Povalej Brzan & Nino Fijacko & Fei Wang & Boris Delibasic & Alexandros Kalousis & Zoran Obradovic, 2015. "Comprehensible Predictive Modeling Using Regularized Logistic Regression and Comorbidity Based Features," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-11, December.
    4. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    5. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    6. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
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