Modeling Diagnostic Label Correlation for Automatic ICD Coding

Abstract

Given the clinical notes written in electronic health records (EHRs), it is challenging to pre- dict 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 bi- nary 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 can- didate generated by a base predictor. This pa- per is the first attempt at learning the label set distribution as a reranking module for medi- cal code prediction. In the experiments, our proposed framework is able to improve upon best-performing predictors on the benchmark MIMIC datasets.