survex: an R package for explaining machine learning survival models

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

survex : an R package for explaining machine learning survival models. / Spytek, Mikołaj; Krzyziński, Mateusz; Langbein, Sophie Hanna; Baniecki, Hubert; Wright, Marvin N.; Biecek, Przemysław.

I: Bioinformatics, Bind 39, Nr. 12, btad723, 2023.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Spytek, M, Krzyziński, M, Langbein, SH, Baniecki, H, Wright, MN & Biecek, P 2023, 'survex: an R package for explaining machine learning survival models', Bioinformatics, bind 39, nr. 12, btad723. https://doi.org/10.1093/bioinformatics/btad723

APA

Spytek, M., Krzyziński, M., Langbein, S. H., Baniecki, H., Wright, M. N., & Biecek, P. (2023). survex: an R package for explaining machine learning survival models. Bioinformatics, 39(12), [btad723]. https://doi.org/10.1093/bioinformatics/btad723

Vancouver

Spytek M, Krzyziński M, Langbein SH, Baniecki H, Wright MN, Biecek P. survex: an R package for explaining machine learning survival models. Bioinformatics. 2023;39(12). btad723. https://doi.org/10.1093/bioinformatics/btad723

Author

Spytek, Mikołaj ; Krzyziński, Mateusz ; Langbein, Sophie Hanna ; Baniecki, Hubert ; Wright, Marvin N. ; Biecek, Przemysław. / survex : an R package for explaining machine learning survival models. I: Bioinformatics. 2023 ; Bind 39, Nr. 12.

Bibtex

@article{e2e22422cd674e80869e7517936e58f7,
title = "survex: an R package for explaining machine learning survival models",
abstract = "Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. 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.",
author = "Miko{\l}aj Spytek and Mateusz Krzyzi{\'n}ski and Langbein, {Sophie Hanna} and Hubert Baniecki and Wright, {Marvin N.} and Przemys{\l}aw Biecek",
note = "Publisher Copyright: {\textcopyright} The Author(s) 2023. Published by Oxford University Press.",
year = "2023",
doi = "10.1093/bioinformatics/btad723",
language = "English",
volume = "39",
journal = "Bioinformatics (Online)",
issn = "1367-4811",
publisher = "Oxford University Press",
number = "12",

}

RIS

TY - JOUR

T1 - survex

T2 - an R package for explaining machine learning survival models

AU - Spytek, Mikołaj

AU - Krzyziński, Mateusz

AU - Langbein, Sophie Hanna

AU - Baniecki, Hubert

AU - Wright, Marvin N.

AU - Biecek, Przemysław

N1 - Publisher Copyright: © The Author(s) 2023. Published by Oxford University Press.

PY - 2023

Y1 - 2023

N2 - Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. 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.

AB - Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. 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.

U2 - 10.1093/bioinformatics/btad723

DO - 10.1093/bioinformatics/btad723

M3 - Journal article

C2 - 38039146

AN - SCOPUS:85181179123

VL - 39

JO - Bioinformatics (Online)

JF - Bioinformatics (Online)

SN - 1367-4811

IS - 12

M1 - btad723

ER -

ID: 379635008