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BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates.
Xiaochen Wang1, Arash Pakbin2, Bobak J Mortazavi2
1Biostatistics Department, Yale University, New Haven, Connecticut, USA.
Proceedings of Machine Learning Research
|February 22, 2021
Summary
This study introduces BoXHED, a new software for dynamic health risk scores using time-varying vitals. It identifies novel interactions in cardiovascular disease risk factors.
Area of Science:
- Biostatistics
- Computational Biology
- Medical Informatics
Background:
- High-frequency medical monitoring enables dynamic health risk scores.
- Survival analysis is suitable for time-varying covariate data.
- Predicting disease onset requires analyzing continuous health data streams.
Purpose of the Study:
- Introduce BoXHED software for nonparametric hazard function estimation.
- Provide the first public software implementation of Lee et al. (2017) estimator.
- Analyze time-dependent covariates for disease prediction.
Main Methods:
- Nonparametric hazard estimation using gradient boosting.
- Tree-based implementation of a generic estimator for time-dependent covariates.
- Application to Framingham Heart Study cardiovascular disease dataset.
Main Results:
- BoXHED enables nonparametric estimation of hazard functions.
- Identified novel interaction effects among cardiovascular disease risk factors.
- Demonstrated utility in analyzing time-varying health data.
Conclusions:
- BoXHED is a valuable tool for analyzing dynamic health data.
- The software may help resolve open questions in clinical literature regarding risk factor interactions.
- Facilitates advanced survival analysis with time-dependent covariates.
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