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BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates

Xiaochen Wang · Arash Pakbin · Bobak Mortazavi · Hongyu Zhao · Donald Lee


Keywords: [ Healthcare ] [ Time Series and Sequence Models ] [ Boosting / Ensemble Methods ] [ Applications - Neuroscience, Cognitive Science, Biology and Health ]


The proliferation of medical monitoring devices makes it possible to track health vitals at high frequency, enabling the development of dynamic health risk scores that change with the underlying readings. Survival analysis, in particular hazard estimation, is well-suited to analyzing this stream of data to predict disease onset as a function of the time-varying vitals. This paper introduces the software package BoXHED (pronounced `box-head') for nonparametrically estimating hazard functions via gradient boosting. BoXHED 1.0 is a novel tree-based implementation of the generic estimator proposed in Lee et al. (2017), which was designed for handling time-dependent covariates in a fully nonparametric manner. BoXHED is also the first publicly available software implementation for Lee et al. (2017). Applying BoXHED to cardiovascular disease onset data from the Framingham Heart Study reveals novel interaction effects among known risk factors, potentially resolving an open question in clinical literature.

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