整合基于规则的NLP和大型语言模型,从临床笔记中提取他类药物信息
Siru Liu1, Allison B McCoy2, Qingyu Chen3
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
International journal of medical informatics
|September 9, 2025
概括
一个新的混合AI框架有效地从临床笔记中识别患者的他类药物治疗障碍,改善心血管保健和临床决策支持 (CDS). 这种人工智能工具准确地提取针对性干预的不耐受性,禁忌和推迟数据.
科学领域:
- 人工智能在医学中的应用
- 临床信息学 临床信息学
- 自然语言处理自然语言处理.
背景情况:
- 识别患者对他类药物治疗的具体障碍对于心血管保健至关重要.
- 从临床笔记中自动提取这些障碍可以增强临床决策支持 (CDS).
研究的目的:
- 开发和评估一种新的混合人工智能 (AI) 框架,以从临床笔记中提取他类药物治疗障碍.
- 准确高效地处理大量的临床数据.
主要方法:
- 开发了一个混合AI框架,结合了基于规则的NLP和基于LLM的过器和分类器.
- 该框架在2000个笔记上进行了培训,并在47192名患者的197,761个笔记上进行了验证.
- 绩效与手册图表审查对他类药物不耐受,禁忌和患者延期进行了评估.
主要成果:
- 人工智能框架表现出高效率,最初的过器删除了超过77%的无关键笔记.
- 在分类不耐受性 (0.99),禁忌 (0.81) 和患者推迟 (0.86) 方面获得了高F1分数.
- 该研究确定了遇到这些障碍的患者的具体百分比:6.4%的不耐受性,0.7%的禁忌和2.9%的延期治疗.
结论:
- 混合AI框架为分析临床笔记提供了一种高效,可扩展和可靠的方法.
- 这种人工智能解决方案可以显著增强临床决策支持系统,改善准则的遵守,并减少提供者的工作负担.
- 未来的工作应该集中在将这些人工智能驱动的见解集成到CDS工具中,以优化他类药物治疗和患者的结果.
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