Year: 2026
Type: conference-paper
Abstract: Transformer-based models such as PLM-CA achieve strong performance for automatic ICD coding, but their attention weights do not provide faithful explanations of their predictions.This is a major limitation for electronic medical records, where users often need concise and trustworthy evidence for each assigned code.To address this issue, we jointly train a sentence extractor and an ICD code classifier such that predictions are based only on the extracted sentences.As a result, the extracted sentences serve as faithful rationales for each predicted code and substantially reduce the effort required to inspect long medical records.Experiments on MIMIC-III show that our method approaches the performance of a transformer baseline that processes the full record while using only a small fraction of the document.
Institutions: Hiroshima University, Doshisha University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W7166813676