BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates

06/25/2020
by   Xiaochen Wang, et al.
0

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, Chen and Ishwaran (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, Chen and Ishwaran (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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

10/17/2021

Real-time Mortality Prediction Using MIMIC-IV ICU Data Via Boosted Nonparametric Hazards

Electronic Health Record (EHR) systems provide critical, rich and valuab...
09/15/2018

ROC-Guided Survival Trees and Forests

Tree-based methods are popular nonparametric tools in studying time-to-e...
01/27/2017

Boosting hazard regression with time-varying covariates

Consider a left-truncated right-censored survival process whose evolutio...
06/05/2018

Predictive Accuracy of Markers or Risk Scores for Interval Censored Survival Data

Methods for the evaluation of the predictive accuracy of biomarkers with...
02/03/2021

pcoxtime: Penalized Cox Proportional Hazard Model for Time-dependent Covariates

The penalized Cox proportional hazard model is a popular analytical appr...
05/03/2021

Leveraging Deep Representations of Radiology Reports in Survival Analysis for Predicting Heart Failure Patient Mortality

Utilizing clinical texts in survival analysis is difficult because they ...
09/18/2020

A Bayesian Time-Varying Effect Model for Behavioral mHealth Data

The integration of mobile health (mHealth) devices into behavioral healt...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.