Timezone: »
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.
Author Information
Xiaochen Wang (Yale University)
Arash Pakbin (Texas A&M University)
Bobak Mortazavi (Texas A&M University)
Hongyu Zhao (Yale University)
Donald Lee (Emory University)
More from the Same Authors
-
2022 Poster: VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty »
Randy Ardywibowo · Zepeng Huo · Zhangyang “Atlas” Wang · Bobak Mortazavi · Shuai Huang · Xiaoning Qian -
2022 Spotlight: VariGrow: Variational Architecture Growing for Task-Agnostic Continual Learning based on Bayesian Novelty »
Randy Ardywibowo · Zepeng Huo · Zhangyang “Atlas” Wang · Bobak Mortazavi · Shuai Huang · Xiaoning Qian -
2021 Poster: Self-Damaging Contrastive Learning »
Ziyu Jiang · Tianlong Chen · Bobak Mortazavi · Zhangyang “Atlas” Wang -
2021 Spotlight: Self-Damaging Contrastive Learning »
Ziyu Jiang · Tianlong Chen · Bobak Mortazavi · Zhangyang “Atlas” Wang