Modeling Diagnostic Label Correlation for Automatic ICD Coding (2021-06-01T00:00:00.000000Z)

TL;DR

This paper is the first attempt at learning the label set distribution as a reranking module for ICD coding, and is able to improve upon best-performing predictors for medical code prediction on the benchmark MIMIC datasets.

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 ICD coding. In the experiments, our proposed framework is able to improve upon best-performing predictors for medical code prediction on the benchmark MIMIC datasets.

Authors

Shang-Chi Tsai

1 papers

Chaorui Huang

1 papers

Yun-Nung Chen

1 papers

TL;DR

Abstract

Authors

References33 items

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