电子健康记录的自然语言处理用于早期检测认知衰退:系统性审查
Ravi Shankar1, Anjali Bundele2,3, Amartya Mukhopadhyay4
1Medical Affairs - Research Innovation & Enterprise, Alexandra Hospital, National University Health System, Singapore, Singapore. Ravi_SHANKAR@nuhs.edu.sg.
NPJ digital medicine
|March 2, 2025
概括
自然语言处理 (NLP) 在临床笔记中有效检测认知障碍. 深度学习模型显示出卓越的性能,但数据和标准化挑战阻碍了广泛的临床使用.
科学领域:
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
- 医疗保健中的人工智能
背景情况:
- 认知障碍的检测对于患者护理至关重要.
- 电子健康记录 (EHR) 包含有价值的临床信息.
- 自然语言处理 (NLP) 提供了对临床笔记的自动化分析的潜力.
研究的目的:
- 系统地审查NLP方法来检测认知障碍在EHR临床笔记.
- 评估各种NLP方法的性能和挑战.
主要方法:
- 按照PRISMA指南进行系统审查.
- 分析了18项涉及100多万名患者记录的研究.
- 将NLP方法分类为基于规则的传统机器学习和深度学习.
主要成果:
- NLP模型显示出高性能:中位灵敏度为0.88,特异性为0.96.
- 深度学习架构取得了出色的结果 (AUC高达0.997).
- 关键的挑战包括数据完整性,文档一致性和验证.
结论:
- 从临床笔记中识别认知衰退,NLP是一个有前途的工具.
- 标准化,更好的数据集和公平的部署对于临床翻译至关重要.
- 需要进一步的研究来克服实施障碍.
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