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However, their widespread adoption is hindered by a lack of user-friendly tools to explain their internal operations and prediction rationales. To tackle this issue, we introduce the survex R package, which provides a cohesive framework for explaining any survival model by applying explainable artificial intelligence techniques. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, transparency and responsibility may be promoted in sensitive areas, such as biomedical research and healthcare applications.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>survex is available under the GPL3 public license at https:\/\/github.com\/modeloriented\/survex and on CRAN with documentation available at https:\/\/modeloriented.github.io\/survex.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btad723","type":"journal-article","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T23:46:29Z","timestamp":1701301589000},"source":"Crossref","is-referenced-by-count":45,"title":["survex: an R package for explaining machine learning survival models"],"prefix":"10.1093","volume":"39","author":[{"given":"Miko\u0142aj","family":"Spytek","sequence":"first","affiliation":[{"name":"MI2.AI, Faculty of Mathematics and Information Science, Warsaw University of Technology , Warsaw, Poland"}]},{"given":"Mateusz","family":"Krzyzi\u0144ski","sequence":"additional","affiliation":[{"name":"MI2.AI, Faculty of Mathematics and Information Science, Warsaw University of Technology , Warsaw, Poland"}]},{"given":"Sophie Hanna","family":"Langbein","sequence":"additional","affiliation":[{"name":"Leibniz Institute for Prevention Research and Epidemiology\u2014BIPS , Bremen, Germany"},{"name":"Faculty of Mathematics and Computer Science, University of Bremen , Bremen, Germany"}]},{"given":"Hubert","family":"Baniecki","sequence":"additional","affiliation":[{"name":"MI2.AI, Faculty of Mathematics and Information Science, Warsaw University of Technology , Warsaw, Poland"},{"name":"MI2.AI, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw , Warsaw, Poland"}]},{"given":"Marvin N","family":"Wright","sequence":"additional","affiliation":[{"name":"Leibniz Institute for Prevention Research and Epidemiology\u2014BIPS , Bremen, Germany"},{"name":"Faculty of Mathematics and Computer Science, University of Bremen , Bremen, Germany"},{"name":"Section of Biostatistics, Department of Public Health, University of Copenhagen , Copenhagen, Denmark"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8423-1823","authenticated-orcid":false,"given":"Przemys\u0142aw","family":"Biecek","sequence":"additional","affiliation":[{"name":"MI2.AI, Faculty of Mathematics and Information Science, Warsaw University of Technology , Warsaw, Poland"},{"name":"MI2.AI, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw , Warsaw, Poland"}]}],"member":"286","published-online":{"date-parts":[[2023,12,1]]},"reference":[{"key":"2024041812221221100_btad723-B1","first-page":"559","author":"Ahmad","year":"2018"},{"key":"2024041812221221100_btad723-B2","doi-asserted-by":"crossref","first-page":"1059","DOI":"10.1111\/rssb.12377","article-title":"Visualizing the effects of predictor variables in black box supervised learning models","volume":"82","author":"Apley","year":"2020","journal-title":"J R Stat Soc Ser B"},{"key":"2024041812221221100_btad723-B3","doi-asserted-by":"crossref","DOI":"10.1007\/s10618-023-00924-w","article-title":"The grammar of interactive explanatory model analysis","author":"Baniecki","year":"2023","journal-title":"Data Min Knowl Disc"},{"key":"2024041812221221100_btad723-B4","first-page":"65","author":"Baniecki","year":"2023"},{"key":"2024041812221221100_btad723-B5","first-page":"1","article-title":"DALEX: explainers for complex predictive models in R","volume":"19","author":"Biecek","year":"2018","journal-title":"J Mach Learn Res"},{"key":"2024041812221221100_btad723-B6","doi-asserted-by":"crossref","DOI":"10.1201\/9780429027192","volume-title":"Explanatory Model Analysis","author":"Biecek","year":"2021"},{"key":"2024041812221221100_btad723-B7","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2024041812221221100_btad723-B8","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.pan.2023.04.009","article-title":"Machine learning versus regression for prediction of sporadic pancreatic cancer","volume":"23","author":"Chen","year":"2023","journal-title":"Pancreatology"},{"key":"2024041812221221100_btad723-B9","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression models and life-tables","volume":"34","author":"Cox","year":"1972","journal-title":"J R Stat Soc. 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