title: A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.
abstract: Predicting an individual's risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records provide an alternative to the longitudinal cohort studies traditionally used to construct risk models, bringing with them both opportunities and challenges. Large sample sizes and detailed covariate histories enable the use of sophisticated machine learning techniques to uncover complex associations and interactions, but observational databases are often 'messy', with high levels of missing data and incomplete patient follow-up. In this paper, we propose an adaptation of the well-known Naive Bayes machine learning approach to time-to-event outcomes subject to censoring. We compare the predictive performance of our method with the Cox proportional hazards model which is commonly used for risk prediction in healthcare populations, and illustrate its application to prediction of cardiovascular risk using an electronic health record dataset from a large Midwest integrated healthcare system.
authors.affiliation: Division of Biostatistics, University of Minnesota, Minneapolis, MN, U.S.A.
authors.forename: Julian
authors.identifier: http://orcid.org/0000-0002-2032-0875
authors.initials: J
authors.lastname: Wolfson
authors.affiliation: Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, U.S.A.
authors.forename: Sunayan
authors.initials: S
authors.lastname: Bandyopadhyay
authors.affiliation: Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN, U.S.A.
authors.forename: Mohamed
authors.initials: M
authors.lastname: Elidrisi
authors.affiliation: HealthPartners Institute for Education and Research, Minneapolis, MN, U.S.A.
authors.forename: Gabriela
authors.initials: G
authors.lastname: Vazquez-Benitez
authors.affiliation: Division of Biostatistics, University of Minnesota, Minneapolis, MN, U.S.A.
authors.forename: David M
authors.initials: DM
authors.lastname: Vock
authors.affiliation: Division of Biostatistics, University of Minnesota, Minneapolis, MN, U.S.A.
authors.forename: Donald
authors.initials: D
authors.lastname: Musgrove
authors.affiliation: Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, MN, U.S.A.
authors.forename: Gediminas
authors.initials: G
authors.lastname: Adomavicius
authors.affiliation: Department of Information and Decision Sciences, Carlson School of Management, University of Minnesota, Minneapolis, MN, U.S.A.
authors.forename: Paul E
authors.initials: PE
authors.lastname: Johnson
authors.affiliation: HealthPartners Institute for Education and Research, Minneapolis, MN, U.S.A.
authors.forename: Patrick J
authors.initials: PJ
authors.lastname: O'Connor
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D002318:Cardiovascular Diseases / Q000453:epidemiology; D003198:Computer Simulation; D016208:Databases, Factual; D019033:Delivery of Health Care, Integrated; D057286:Electronic Health Records; D006801:Humans; D008137:Longitudinal Studies; D000069550:Machine Learning; D015146:Midwestern United States / Q000453:epidemiology; D016016:Proportional Hazards Models; D012306:Risk; D018570:Risk Assessment / Q000379:methods*; D013029:Space-Time Clustering
keywords: Naive Bayes; electronic health records; machine learning; risk prediction; survival analysis