Modeling Diagnostic Label Correlation for Automatic ICD Coding

Shang-Chi Tsai, Chao-Wei Huang, Yun-Nung Chen


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.
Anthology ID:
2021.naacl-main.318
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Editors:
Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, Yichao Zhou
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4043–4052
Language:
URL:
https://aclanthology.org/2021.naacl-main.318
DOI:
10.18653/v1/2021.naacl-main.318
Bibkey:
Cite (ACL):
Shang-Chi Tsai, Chao-Wei Huang, and Yun-Nung Chen. 2021. Modeling Diagnostic Label Correlation for Automatic ICD Coding. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4043–4052, Online. Association for Computational Linguistics.
Cite (Informal):
Modeling Diagnostic Label Correlation for Automatic ICD Coding (Tsai et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.318.pdf
Video:
 https://aclanthology.org/2021.naacl-main.318.mp4
Code
 MiuLab/ICD-Correlation