评估一种以自然语言处理驱动,人工智能辅助的疾病国际分类,第10版,临床修改,诊断相关群体的编码系统在现实医院环境中:算法开发和验证研究研究.
Hong-Jie Dai1,2,3, Chen-Kai Wang1,4,5, Chien-Chang Chen6
1Intelligent System Lab, College of Electrical Engineering and Computer Science, Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung, Taiwan.
Journal of medical Internet research
|September 20, 2024
概括
一个由人工智能驱动的自然语言处理系统协助编码国际疾病分类,第十版,临床修改 (ICD-10-CM),改进台湾诊断相关组 (Tw-DRGs) 评估,并减少经过认证的编码专家的手工工作量.
科学领域:
- 医疗信息学医学信息学
- 医疗保健中的人工智能
- 自然语言处理 (NLP) 是一种自然语言处理.
背景情况:
- 手动国际疾病分类 (ICD) 编码是复杂的,耗时的,容易出现错误,尤其是在过渡到ICD-10时.
- 不准确的编码导致财务损失,并影响台湾诊断相关组 (Tw-DRGs) 的准确性.
- 自动编码系统对于提高医疗诊断编码的效率和准确性至关重要.
研究的目的:
- 评估ICD-10-CM自编码系统的可行性,使用自由文本排放摘要.
- 评估系统自动确定Tw-DRG的主要诊断和主要诊断类别 (MDC) 的能力.
主要方法:
- 开发了人工智能辅助的ICD-10-CM编码系统,使用在出院摘要上的深度学习模型.
- 创建了一个基于Web的用户界面,并部署了认证编码专家 (CCS) 的系统.
- 使用参考和真实医院数据评估系统性能,用于ICD-10-CM编码和Tw-DRG确定.
主要成果:
- 在测试数据中,GPT-2模型获得了最高的F1得分 (总体为0.667,前50个代码为0.851).
- 对于主要诊断类别 (MDCs),GPT-2与CCS达成优异一致,平均科恩卡帕为0.714.
- 人工智能系统表现一致,支持ICD-10-CM编码和Tw-DRG评估中的CCS.
结论:
- 由NLP驱动的AI辅助编码系统可以有效地支持ICD-10-CM编码中的CCS.
- 该系统有可能减少手工工作量并加快Tw-DRG评估.
- 开发的系统在ICD-10-CM编码和Tw-DRG判断方面都表现出有效性.
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