Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation
<|reference_start|>Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding Recommendation: The International Classification of Diseases (ICD) serves as a definitive medical classification system encompassing a wide range of diseases and conditions. The primary objective of ICD indexing is to allocate a subset of ICD codes to a medical record, which facilitates standardized documentation and management of various health conditions. Most existing approaches have suffered from selecting the proper label subsets from an extremely large ICD collection with a heavy long-tailed label distribution. In this paper, we leverage a multi-stage ``retrieve and re-rank'' framework as a novel solution to ICD indexing, via a hybrid discrete retrieval method, and re-rank retrieved candidates with contrastive learning that allows the model to make more accurate predictions from a simplified label space. The retrieval model is a hybrid of auxiliary knowledge of the electronic health records (EHR) and a discrete retrieval method (BM25), which efficiently collects high-quality candidates. In the last stage, we propose a label co-occurrence guided contrastive re-ranking model, which re-ranks the candidate labels by pulling together the clinical notes with positive ICD codes. Experimental results show the proposed method achieves state-of-the-art performance on a number of measures on the MIMIC-III benchmark.<|reference_end|>
@article{wang2024multi-stage,
    title={Multi-stage Retrieve and Re-rank Model for Automatic Medical Coding
  Recommendation},
    author={Xindi Wang, Robert E. Mercer, Frank Rudzicz},
    journal={arXiv preprint arXiv:2405.19093},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.19093},
    primaryClass={cs.CL cs.IR}
}