Continuous predictive modeling of clinical notes and ICD codes in patient health records
File(s)
Accepted version
Author(s)
Caralt, MH
Ng, CBL
Rei, M
Type
Conference Paper
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..
Date Issued
2024-08-16
Date Acceptance
2024-08-01
Citation
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, 2024, pp.243-255
Publisher
Association for Computational Linguistics
Start Page
243
End Page
255
Journal / Book Title
Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
Copyright Statement
©2024 Association for Computational Linguistics.
Source
BioNIP 2024 23rd Meeting of the ACLSpecial Interest Group on Biomedical Natural Language Processing
Publication Status
Published
Start Date
2024-08-16
Coverage Spatial
Bangkok, Thailand