{"title":"BART-Survival：在Python中使用贝叶斯机器学习方法进行生存分析。","authors":"Jacob Tiegs, Julia Raykin, Ilia Rochlin","doi":"10.21105/joss.07213","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":94101,"journal":{"name":"Journal of open source software","volume":"10 105","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848778/pdf/","citationCount":"0","resultStr":"{\"title\":\"BART-Survival: A Bayesian machine learning approach to survival analyses in Python.\",\"authors\":\"Jacob Tiegs, Julia Raykin, Ilia Rochlin\",\"doi\":\"10.21105/joss.07213\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":94101,\"journal\":{\"name\":\"Journal of open source software\",\"volume\":\"10 105\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11848778/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of open source software\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21105/joss.07213\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of open source software","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21105/joss.07213","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
BART-Survival: A Bayesian machine learning approach to survival analyses in Python.
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.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
