Real time risk prognostication via scalable hazard trees and forests

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Project Summary/Abstract Wearable sensing devices and Electronic Health Records (EHRs) are some examples of emerging information technologies expected to generate huge volumes of data recording individual’s health data over time. If properly utilized, these data provide a treasure trove of information for building real-time warning systems for adverse outcomes and to construct individualized risk prediction. To model the dynamic changes of covariate effects, time-varying survival models have emerged as a powerful approach. To deal with the size and complexity of data, with potential interactions among large number of variables, and interactions with time itself, we propose a state of the art machine learning approach using hazard trees and forests for estimating flexible hazard models with time-dependent covariates. Scalable and user friendly open source software implementing the methodology will be developed and made publicly available. The software will be applied to a rich, multicenter study of heart failure patients listed for heart transplantation to develop a state of the heart hazard risk prediction model.