[2402.00072] Explainable AI for survival analysis: a median-SHAP approach
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Computer Science > Machine Learning

arXiv:2402.00072 (cs)
[Submitted on 30 Jan 2024]

Title:Explainable AI for survival analysis: a median-SHAP approach

Authors:Lucile Ter-Minassian, Sahra Ghalebikesabi, Karla Diaz-Ordaz, Chris Holmes
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Abstract:With the adoption of machine learning into routine clinical practice comes the need for Explainable AI methods tailored to medical applications. Shapley values have sparked wide interest for locally explaining models. Here, we demonstrate their interpretation strongly depends on both the summary statistic and the estimator for it, which in turn define what we identify as an 'anchor point'. We show that the convention of using a mean anchor point may generate misleading interpretations for survival analysis and introduce median-SHAP, a method for explaining black-box models predicting individual survival times.
Comments: Accepted to the Interpretable Machine Learning for Healthcare (IMLH) workshop of the ICML 2022 Conference
Subjects: Machine Learning (cs.LG); Methodology (stat.ME); Machine Learning (stat.ML)
Cite as: arXiv:2402.00072 [cs.LG]
  (or arXiv:2402.00072v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2402.00072

Submission history

From: Lucile Ter-Minassian [view email]
[v1] Tue, 30 Jan 2024 20:47:50 UTC (4,916 KB)
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