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arXiv · 2104.07894

Faithful and Plausible Explanations of Medical Code Predictions

Zach Wood-Doughty·Isabel Cachola·Mark Dredze
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Abstract

Machine learning models that offer excellent predictive performance often lack the interpretability necessary to support integrated human machine decision-making. In clinical medicine and other high-risk settings, domain experts may be unwilling to trust model predictions without explanations. Work in explainable AI must balance competing objectives along two different axes: 1) Explanations must balance faithfulness to the model's decision-making with their plausibility to a domain expert. 2) Domain experts desire local explanations of individual predictions and global explanations of behavior in aggregate. We propose to train a proxy model that mimics the behavior of the trained model and provides fine-grained control over these trade-offs. We evaluate our approach on the task of assigning ICD codes to clinical notes to demonstrate that explanations from the proxy model are faithful and replicate the trained model behavior.

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Zach Wood-Doughty, Isabel Cachola, Mark Dredze. 2021-04-16. Faithful and Plausible Explanations of Medical Code Predictions. https://arxiv.org/abs/2104.07894

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