@inproceedings{baksi-etal-2025-medcoder,
    title = "{M}ed{C}od{ER}: A Generative {AI} Assistant for Medical Coding",
    author = "Baksi, Krishanu Das  and
      Soba, Elijah  and
      Higgins, John J  and
      Saini, Ravi  and
      Wood, Jaden  and
      Cook, Jane  and
      Scott, Jack I  and
      Pudota, Nirmala  and
      Weninger, Tim  and
      Bowen, Edward  and
      Bhattacharya, Sanmitra",
    editor = "Chen, Weizhu  and
      Yang, Yi  and
      Kachuee, Mohammad  and
      Fu, Xue-Yong",
    booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)",
    month = apr,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.naacl-industry.37/",
    pages = "449--459",
    ISBN = "979-8-89176-194-0",
    abstract = "Medical coding standardizes clinical data but is both time-consuming and error-prone. Traditional Natural Language Processing (NLP) methods struggle with automating coding due to the large label space, lengthy text inputs, and the absence of supporting evidence annotations that justify code selection. Recent advancements in Generative Artificial Intelligence (AI) offer promising solutions to these challenges. In this work, we introduce MedCodER, an emerging Generative AI framework for automatic medical coding that leverages extraction, retrieval, and re-ranking techniques as core components. MedCodER achieves a micro-F1 score of 0.62 on International Classification of Diseases (ICD) code prediction, significantly outperforming state-of-the-art methods. Additionally, we present a new dataset containing medical records annotated with disease diagnoses, ICD codes, and supporting evidence texts (https://doi.org/10.5281/zenodo.13308316). Ablation tests confirm that MedCodER{'}s performance depends on the integration of each of its aforementioned components, as performance declines when these components are evaluated in isolation."
}