Evaluating Local Interpretable Model-Agnostic Explanations on Clinical Machine Learning Classification Models

Abstract

The usage of black-box classification models within the healthcare field is highly dependent on being interpretable by the receiver. Local Interpretable Model-Agnostic Explanation (LIME) provides a patient-specific explanation for a given classification, thus enhancing the possibility for any complex classifier to serve as a safety aid within a clinical setting. However, research on if the explanation provided by LIME is relevant to clinicians is limited and there is no current framework for how an evaluation of LIME is to be performed. To evaluate the clinical relevance of the explanations provided by LIME, this study has investigated how physician's independent explanations for classified observations compare with explanations provided by LIME. Additionally, the clinical relevance and the experienced reliance on the explanations provided by LIME have been evaluated by field experts. The results indicate that the explanation provided by LIME is clinically relevant and has a very high concordance with the explanations provided by physicians. Furthermore, trust and reliance on LIME are fairly high amongst clinicians. The study proposes a framework for further research within the area.

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