BoXHED: Boosted eXact Hazard Estimator with Dynamic covariates.

Xiaochen Wang, Arash Pakbin, Bobak J Mortazavi, Hongyu Zhao, Donald K K Lee

Abstract read

In one paragraph

Article in Proceedings of machine learning research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

BoXHED2.0: Scalable Boosting of Dynamic Survival Analysis.Journal of statistical software · 2025
Article
Frontiers in Operations: Valuing Nursing Productivity in Emergency Departments.Manufacturing & service operations management : M & SOM
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Xiaochen WangBiostatistics Department, Yale University, New Haven, Connecticut, USA.

Arash PakbinComputer Science & Engineering, Texas A&M University, College Station, Texas, USA.

Bobak J MortazaviComputer Science & Engineering, Texas A&M University, College Station, Texas, USA.

Hongyu ZhaoBiostatistics Department, Yale University, New Haven, Connecticut, USA.

Donald K K LeeGoizueta Business School and Department of Biostatistics & Bioinformatics, Emory University, Atlanta, Georgia, USA.

Funding

Estimating Trajectory of Recovery in Cardiac Rehabilitation using Mobile Health Technology and Personalized Machine LearningR21EB028486 · NIBIB · TEXAS ENGINEERING EXPERIMENT STATION · PI MORTAZAVI, BOBAK JACK · 2019 to 2021

$563k

NIBIB NIH HHS R21 EB028486

6 · The paper itself

Abstract

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 it to a cardiovascular disease dataset from the Framingham Heart Study reveals novel interaction effects among known risk factors, potentially resolving an open question in clinical literature.

Identifiers

PMID33615237

PMCPMC7890797

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.