实施和评估ICD-10人工智能编码辅助系统
Ming-Chuan Kuo1,2, Yi-Jhen Lin1, Yi-Min Lee3
1Cathay General Hospital, Taipei City, Taiwan.
Studies in health technology and informatics
|August 8, 2025
概括
疾病国际统计分类第10版 (ICD-10) 提出了编码方面的挑战. 本研究介绍了一种使用深度学习和NLP的自动化系统,以提高医疗编码的准确性和效率.
科学领域:
- 医疗信息学 医疗信息学
- 卫生信息系统 卫生信息系统
- 医疗保健中的人工智能
背景情况:
- 世界卫生组织的疾病国际统计分类第10版 (ICD-10) 是疾病分类的全球标准.
- 台湾在2016年采用ICD-10,其广泛的~150,000个代码,与ICD-9 (~17,000个代码) 相比,增加了复杂性.
- 复杂性增加导致医生工作量挑战,并可能低估患者的风险因素,由于不完整的诊断.
研究的目的:
- 开发一种自动化的疾病编码推系统.
- 利用深度学习和自然语言处理 (NLP) 来增强医疗编码.
- 提高医疗记录文档的质量和效率,减少编码员的工作量.
主要方法:
- 实施深度学习算法,用于医学数据中的模式识别.
- 应用自然语言处理 (NLP) 技术来解释临床叙述.
- 开发推系统,以协助医生进行ICD-10编码.
主要成果:
- 拟议的系统旨在帮助医生做出准确的编码决策.
- 预计它将优化健康信息系统 (HIS) 接口设计,以提高可用性.
- 预期的结果包括改善文档完整性和减少医生花在编码上的时间.
结论:
- 一个自动化的ICD-10编码推系统可以解决复杂的编码标准所带来的挑战.
- 整合人工智能技术可以显著减轻与医疗索赔相关的工作量.
- 这种方法有望提高医疗数据管理的整体质量和效率.
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