SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models

Utkin, Lev V.; Kovalev, Maxim S.; Kasimov, Ernest M.

doi:10.48550/arxiv.2005.02387

Cited by 2 publications

(4 citation statements)

References 49 publications

(60 reference statements)

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“…The interpretation of the explainer method should therefore be limited to obtaining a summary of the behavior of the predictions from a black-box model. 59 In comparison to the existing explainable methods for survival prediction models using LIME, 18,19,35 the presented approach allows for obtaining time-specific explanations and does not rely on a restrictive proportional hazards explanation model. Compared to SurvSHAP(t), 20 we also extend an existing explainer for classification to capture time-dependent effects in a survival model.…”

Section: Discussionmentioning

confidence: 99%

“…SurvLIME has been further extended to improve computational efficiency and accommodate data intricacies. SurvLIME-Inf 19 uses L ∞ -norm in the optimization of the distance between the prediction and explainer models to reduce it to a simple linear programming problem. SurvLIME-KS 34 uses Kolmogorov-Smirnov bounds to modify SurvLIME to obtain robust explanations when the training data set is small and contains outliers.…”

Section: Explainable Machine Learning and Survivalmentioning

confidence: 99%

“…Existing explainer methods for survival prediction models provide an explanation for the effect of an individual's predictors on their overall survival curve. 18,19 However, these methods assume that the covariate effects satisfy the proportional hazards assumption, which may not be reflective of the behavior of the predictive model. In recent literature, 20 an extension of SHAP has been presented that can capture time-varying covariate effects and takes advantage of its properties of local accuracy and consistency.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model‐agnostic explanations for survival prediction models

Suresh,

Görg,

Ghosh

2024

Statistics in Medicine

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Advanced machine learning methods capable of capturing complex and nonlinear relationships can be used in biomedical research to accurately predict time‐to‐event outcomes. However, these methods have been criticized as “black boxes” that are not interpretable and thus are difficult to trust in making important clinical decisions. Explainable machine learning proposes the use of model‐agnostic explainers that can be applied to predictions from any complex model. These explainers describe how a patient's characteristics are contributing to their prediction, and thus provide insight into how the model is arriving at that prediction. The specific application of these explainers to survival prediction models can be used to obtain explanations for (i) survival predictions at particular follow‐up times, and (ii) a patient's overall predicted survival curve. Here, we present a model‐agnostic approach for obtaining these explanations from any survival prediction model. We extend the local interpretable model‐agnostic explainer framework for classification outcomes to survival prediction models. Using simulated data, we assess the performance of the proposed approaches under various settings. We illustrate application of the new methodology using prostate cancer data.

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Section: Discussionmentioning

confidence: 99%

Section: Explainable Machine Learning and Survivalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Model‐agnostic explanations for survival prediction models

Suresh,

Görg,

Ghosh

2024

Statistics in Medicine

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“…SurvLIME used the quadratic norm to take into account the distance between CHFs. Following ideas underlying SurvLIME, Utkin et al [62] proposed a modification of SurvLIME called SurvLIME-Inf. In contrast to SurvLIME, SurvLIME-Inf uses L ∞ -norm for defining distances between CHFs.…”

Section: Introductionmentioning

confidence: 99%

Counterfactual explanation of machine learning survival models

Kovalev¹,

Utkin²

2020

Preprint

Self Cite

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A method for counterfactual explanation of machine learning survival models is proposed. One of the difficulties of solving the counterfactual explanation problem is that the classes of examples are implicitly defined through outcomes of a machine learning survival model in the form of survival functions. A condition that establishes the difference between survival functions of the original example and the counterfactual is introduced. This condition is based on using a distance between mean times to event. It is shown that the counterfactual explanation problem can be reduced to a standard convex optimization problem with linear constraints when the explained black-box model is the Cox model. For other black-box models, it is proposed to apply the well-known Particle Swarm Optimization algorithm. A lot of numerical experiments with real and synthetic data demonstrate the proposed method.

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SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models

Cited by 2 publications

References 49 publications

Model‐agnostic explanations for survival prediction models

Model‐agnostic explanations for survival prediction models

Counterfactual explanation of machine learning survival models

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