  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.
