Modeling Diagnostic Label Correlation for Automatic ICD Coding

• URL: http://arxiv.org/abs/2106.12800v1
• Date: Thu, 24 Jun 2021 07:26:30 GMT
• Title: Modeling Diagnostic Label Correlation for Automatic ICD Coding
• Authors: Shang-Chi Tsai, Chao-Wei Huang, Yun-Nung Chen
• Abstract summary: We propose a two-stage framework to improve automatic ICD coding by capturing the label correlation. Specifically, we train a label set distribution estimator to rescore the probability of each label set candidate. In the experiments, our proposed framework is able to improve upon best-performing predictors on the benchmark MIMIC datasets.
• Score: 37.79764232289666
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Given the clinical notes written in electronic health records (EHRs), it is challenging to predict the diagnostic codes which is formulated as a multi-label classification task. The large set of labels, the hierarchical dependency, and the imbalanced data make this prediction task extremely hard. Most existing work built a binary prediction for each label independently, ignoring the dependencies between labels. To address this problem, we propose a two-stage framework to improve automatic ICD coding by capturing the label correlation. Specifically, we train a label set distribution estimator to rescore the probability of each label set candidate generated by a base predictor. This paper is the first attempt at learning the label set distribution as a reranking module for medical code prediction. In the experiments, our proposed framework is able to improve upon best-performing predictors on the benchmark MIMIC datasets. The source code of this project is available at https://github.com/MiuLab/ICD-Correlation.

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