This study presents a novel two-stage Retrieve-Rank system for automated ICD-10-CM medical coding, comparing its performance against a Vanilla Large Language Model (LLM) approach. Evaluating both systems on a dataset of 100 single-term medical conditions, the Retrieve-Rank system achieved 100% accuracy in predicting correct ICD-10-CM codes, significantly outperforming the Vanilla LLM (GPT-3.5-turbo), which achieved only 6% accuracy. Our analysis demonstrates the Retrieve-Rank system's superior precision in handling various medical terms across different specialties. While these results are promising, we acknowledge the limitations of using simplified inputs and the need for further testing on more complex, realistic medical cases. This research contributes to the ongoing effort to improve the efficiency and accuracy of medical coding, highlighting the importance of retrieval-based approaches.

该研究介绍了一种用于自动ICD-10-CM医学编码的新型两阶段检索-排序系统，并将其性能与Vanilla Large Language Model (LLM)方法进行了比较。在对100个单项医疗状况的数据集上评估两个系统时，检索-排序系统在预测正确ICD-10-CM代码方面达到了100%的准确率，显著优于只有6%准确率的Vanilla LLM (GPT-3.5-turbo)。研究分析表明，检索-排序系统在处理不同专业的各种医学术语方面具有更高的精确性。尽管这些结果令人鼓舞，我们认识到使用简化输入的局限性以及进一步在更复杂、现实的医疗案例上进行测试的需求。这项研究为提高医学编码的效率和准确性的持续努力做出了贡献，突显了基于检索的方法的重要性。

大型语言模型是优秀的医学编码器，只要提供合适的工具