title: Exploring joint disease risk prediction.
abstract: Disease risk prediction has been a central topic of medical informatics. Although various risk prediction models have been studied in the literature, the vast majority were designed to be single-task, i.e. they only consider one target disease at a time. This becomes a limitation when in practice we are dealing with two or more diseases that are related to each other in terms of sharing common comorbidities, symptoms, risk factors, etc., because single-task prediction models are not equipped to identify these associations across different tasks. In this paper we address this limitation by exploring the application of multi-task learning framework to joint disease risk prediction. Specifically, we characterize the disease relatedness by assuming that the risk predictors underlying these diseases have overlap. We develop an optimization-based formulation that can simultaneously predict the risk for all diseases and learn the shared predictors. Our model is applied to a real Electronic Health Record (EHR) database with 7,839 patients, among which 1,127 developed Congestive Heart Failure (CHF) and 477 developed Chronic Obstructive Pulmonary Disease (COPD). We demonstrate that a properly designed multi-task learning algorithm is viable for joint disease risk prediction and it can discover clinical insights that single-task models would overlook.
authors.affiliation: IBM T. J. Watson Research Center, Yorktown Heights, NY.
authors.forename: Xiang
authors.initials: X
authors.lastname: Wang
authors.affiliation: IBM T. J. Watson Research Center, Yorktown Heights, NY.
authors.forename: Fei
authors.initials: F
authors.lastname: Wang
authors.affiliation: IBM T. J. Watson Research Center, Yorktown Heights, NY.
authors.forename: Jianying
authors.initials: J
authors.lastname: Hu
authors.affiliation: IBM T. J. Watson Research Center, Yorktown Heights, NY.
authors.forename: Robert
authors.initials: R
authors.lastname: Sorrentino
mesh_terms: D000465:Algorithms; D015897:Comorbidity; D057286:Electronic Health Records; D006333:Heart Failure; D006801:Humans; D029424:Pulmonary Disease, Chronic Obstructive; D018570:Risk Assessment / Q000379:methods