@inproceedings{wang-etal-2026-interpretable-icd,
    title = "Interpretable {ICD} Code Classification with Faithful Sentence Extraction",
    author = "Wang, Yichen  and
      Hong, Lian  and
      Mizogaki, Masato  and
      Umeda, Shunnosuke  and
      Kenmotsu, Toshimune  and
      Tamura, Akihiro  and
      Andrade, Daniel",
    editor = "Demner-Fushman, Dina  and
      Ananiadou, Sophia  and
      Roberts, Kirk  and
      Tsujii, Junichi",
    booktitle = "{B}io{NLP} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.bionlp-1.54/",
    pages = "679--686",
    ISBN = "979-8-89176-434-7",
    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."
}