Intriguing Effect of the Correlation Prior on ICD-9 Code Assignment

Zihao Yang, Chenkang Zhang, Muru Wu, Xujin Chris Liu, Lavender Yao Jiang, Kyunghyun Cho, Eric Karl Oermann

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The Ninth Revision of the International Classification of Diseases (ICD-9) is a standardized coding system used to classify health conditions. It is used for billing, tracking individual patient conditions, and for epidemiology. The highly detailed and technical nature of the codes and their associated medical conditions make it difficult for humans to accurately record them. Researchers have explored the use of neural networks, particularly language models, for automated ICD-9 code assignment. However, the imbalanced distribution of ICD-9 codes leads to poor performance. One solution is to use domain knowledge to incorporate a useful prior. This paper evaluates the usefulness of the correlation bias: we hypothesize that correlations between ICD-9 codes and other medical codes could help improve language models' performance. We showed that while the correlation bias worsens the overall performance, the effect on individual class can be negative or positive. Performance on classes that are more imbalanced and less correlated with other codes is more sensitive to incorporating the correlation bias. This suggests that while the correlation bias has potential to improve ICD-9 code assignment in certain cases, the applicability criteria need to be more carefully studied.

Original languageEnglish (US)
Title of host publicationStudent Research Workshop
PublisherAssociation for Computational Linguistics (ACL)
Number of pages10
ISBN (Electronic)9781959429692
StatePublished - 2023
Event61st Annual Meeting of the Association for Computational Linguistics, ACL-SRW 2023 - Toronto, Canada
Duration: Jul 10 2023Jul 12 2023

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X


Conference61st Annual Meeting of the Association for Computational Linguistics, ACL-SRW 2023

ASJC Scopus subject areas

  • Computer Science Applications
  • Linguistics and Language
  • Language and Linguistics


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