Continuous Predictive Modeling of Clinical Notes and ICD Codes in Patient Health Records

Mireia Hernandez Caralt, Clarence Boon Liang Ng, Marek Rei

Abstract
Electronic Health Records (EHR) serve as a valuable source of patient information, offering insights into medical histories, treatments, and outcomes. Previous research has developed systems for detecting applicable ICD codes that should be assigned while writing a given EHR document, mainly focusing on discharge summaries written at the end of a hospital stay. In this work, we investigate the potential of predicting these codes for the whole patient stay at different time points during their stay, even before they are officially assigned by clinicians. The development of methods to predict diagnoses and treatments earlier in advance could open opportunities for predictive medicine, such as identifying disease risks sooner, suggesting treatments, and optimizing resource allocation. Our experiments show that predictions regarding final ICD codes can be made already two days after admission and we propose a custom model that improves performance on this early prediction task.
Anthology ID:
2024.bionlp-1.19
Volume:
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Dina Demner-Fushman, Sophia Ananiadou, Makoto Miwa, Kirk Roberts, Junichi Tsujii
Venues:
BioNLP | WS
SIG:
SIGBIOMED
Publisher:
Association for Computational Linguistics
Note:
Pages:
243–255
Language:
URL:
https://aclanthology.org/2024.bionlp-1.19
DOI:
10.18653/v1/2024.bionlp-1.19
Bibkey:
Cite (ACL):
Mireia Hernandez Caralt, Clarence Boon Liang Ng, and Marek Rei. 2024. Continuous Predictive Modeling of Clinical Notes and ICD Codes in Patient Health Records. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 243–255, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Continuous Predictive Modeling of Clinical Notes and ICD Codes in Patient Health Records (Caralt et al., BioNLP-WS 2024)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.bionlp-1.19.pdf
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