Open Access
December 2019 Hierarchical infinite factor models for improving the prediction of surgical complications for geriatric patients
Elizabeth Lorenzi, Ricardo Henao, Katherine Heller
Ann. Appl. Stat. 13(4): 2637-2661 (December 2019). DOI: 10.1214/19-AOAS1292


Nearly a third of all surgeries performed in the United States occur for patients over the age of 65; these older adults experience a higher rate of postoperative morbidity and mortality. To improve the care for these patients, we aim to identify and characterize high risk geriatric patients to send to a specialized perioperative clinic while leveraging the overall surgical population to improve learning. To this end, we develop a hierarchical infinite latent factor model (HIFM) to appropriately account for the covariance structure across subpopulations in data. We propose a novel Hierarchical Dirichlet Process shrinkage prior on the loadings matrix that flexibly captures the underlying structure of our data while sharing information across subpopulations to improve inference and prediction. The stick-breaking construction of the prior assumes an infinite number of factors and allows for each subpopulation to utilize different subsets of the factor space and select the number of factors needed to best explain the variation. We develop the model into a latent factor regression method that excels at prediction and inference of regression coefficients. Simulations validate this strong performance compared to baseline methods. We apply this work to the problem of predicting surgical complications using electronic health record data for geriatric patients and all surgical patients at Duke University Health System (DUHS). The motivating application demonstrates the improved predictive performance when using HIFM in both area under the ROC curve and area under the PR Curve while providing interpretable coefficients that may lead to actionable interventions.


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Elizabeth Lorenzi. Ricardo Henao. Katherine Heller. "Hierarchical infinite factor models for improving the prediction of surgical complications for geriatric patients." Ann. Appl. Stat. 13 (4) 2637 - 2661, December 2019.


Received: 1 July 2018; Revised: 1 May 2019; Published: December 2019
First available in Project Euclid: 28 November 2019

zbMATH: 07160953
MathSciNet: MR4037444
Digital Object Identifier: 10.1214/19-AOAS1292

Keywords: Bayesian factor model , health care , hierarchical modeling , nonparametrics , surgical outcomes , transfer learning

Rights: Copyright © 2019 Institute of Mathematical Statistics

Vol.13 • No. 4 • December 2019
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