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
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.naacl-main.318.pdf
- Code
- MiuLab/ICD-Correlation