BART-Survival: a Bayesian Machine Learning Approach to Survival Analyses in Python.

Details

• Alternative Title:
  Journal of Open Source Software, 2025, v. 10, no. 105: BART-Survival: a Bayesian Machine Learning Approach to Survival Analyses in Python.
• Journal Article:
  Journal of Open Source Software
• Personal Author:
  Tiegs, Jacob ; Raykin, Julia ; Rochlin, Ilia
• Description:
  BART-Survival is a Python package that allows time-to-event (survival) analyses in discrete-time using the non-parametric machine learning algorithm, Bayesian Additive Regression Trees (BART). BART-Survival combines the performance of the BART algorithm with the complementary data and model formatting required to complete the survival analyses. The library contains a convenient application programming interface (API) that allows a simple approach when using the library for survival analyses, while maintaining capabilities for added complexity when desired. The package is intended for analysts exploring use of flexible non-parametric alternatives to traditional (semi-)parametric survival analyses.
• Content Notes:
  Author manuscript
• Subjects:
  • Bayes Theorem
  • Survival Analysis
  • Machine Learning
  • Algorithms
• Source:
  Journal of Open Source Software, 2025, v. 10, no. 105
• DOI:
  https://doi.org/10.21105/joss.07213
• ISSN:
  2475-9066
• Pubmed ID:
  39995849
• Pubmed Central ID:
  PMC11848778
• Document Type:
  Text
• Funding:
  CC999999/ImCDC/Intramural CDC HHSUnited States/
• Genre:
  Journal Article
• Pages in Document:
  4 pdf pages
• Volume:
  10
• Issue:
  105
• Download URL:
  https://stacks.cdc.gov/view/cdc/261655/cdc_261655_DS1.pdf
• File Type:
  [PDF - 41.47 KB]
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha-512:3670fa38c65bbe8681bc7b64976c9f5d5192e90bde0ff8fd7462e1886d7b13ef84b1cd1ec2d3597439a17d2c3f3ab7ab9d8880d603b951fc6b2e3b9ae649817e