Autonomous International Classification of Diseases Coding Using Pretrained Language Models and Advanced Prompt Learning Techniques: Evaluation of an Automated Analysis System Using Medical Text
These findings underscore the effectiveness and superior performance of prompt learning and fine-tuning for subtasks within pretrained language models in medical practice. Our real-time ICD coding pipeline efficiently converts detailed medical free text into standardized labels, offering promising applications in clinical decision-making. It can assist doctors unfamiliar with the ICD coding system in organizing medical record information, thereby accelerating the medical process and enhancing the efficiency of diagnosis and treatment.
Authors
- Kunlun He (ORCID: https://orcid.org/0000-0002-3335-5700)
- Xiuxing Li (ORCID: https://orcid.org/0000-0002-1178-7422)
- J. Zhang (ORCID: https://orcid.org/0000-0003-3830-9786)
- Yue Yu (ORCID: https://orcid.org/0000-0002-9865-2212)
- Yan Zhuang (ORCID: https://orcid.org/0000-0003-3483-0988)
- Chao Liu (ORCID: https://orcid.org/0000-0002-8960-661X)
- Wei Dong (ORCID: https://orcid.org/0000-0003-4525-1105)
Institutions
- Beijing Institute of Technology (CN)
- Chinese PLA General Hospital (CN)
- Digital China Health (China) (CN)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2024-11-19
- DOI
- https://doi.org/10.2196/63020
- Citations
- 5
- Primary Topic
- Medical Coding and Health Information
- Type
- article
- Field-Weighted Citation Impact
- 3.54