DLIME uses agglomerative hierarchical clustering and KNN to generate stable local explanations for black-box ML predictions on medical data, outperforming LIME on Jaccard similarity of repeated explanations.
Machine Learning Model Interpretability for Precision Medicine
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abstract
Interpretability of machine learning models is critical for data-driven precision medicine efforts. However, highly predictive models are generally complex and are difficult to interpret. Here using Model-Agnostic Explanations algorithm, we show that complex models such as random forest can be made interpretable. Using MIMIC-II dataset, we successfully predicted ICU mortality with 80% balanced accuracy and were also were able to interpret the relative effect of the features on prediction at individual level.
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cs.LG 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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DLIME: A Deterministic Local Interpretable Model-Agnostic Explanations Approach for Computer-Aided Diagnosis Systems
DLIME uses agglomerative hierarchical clustering and KNN to generate stable local explanations for black-box ML predictions on medical data, outperforming LIME on Jaccard similarity of repeated explanations.