Evaluation of LIME and SHAP in Explaining Automatic ICD-10 Classifications of Swedish Gastrointestinal Discharge Summaries
Keywords:ICD-10 diagnosis code, Natural language processing, eXplainable AI, Multi-label text classification
AbstractA computer-assisted coding tool could alleviate the burden on medical staff to assign ICD diagnosis codes to discharge summaries by utilising deep learning models to generate recommendations. However, the opaque nature of deep learning models makes it hard for humans to trust them. In this study, the explainable AI models LIME and SHAP have been applied to the clinical language model SweDeClin-BERT to explain ICD-10 codes assigned to Swedish gastrointestinal discharge summaries. The explanations have been evaluated by eight medical experts, showing a statistically higher significant difference in explainable performance for SHAP compared to LIME.
Copyright (c) 2022 Alexander Dolk, Hjalmar Davidsen, Hercules Dalianis, Thomas Vakili
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