超越电子健康记录数据:利用自然语言处理和机器学习来发现患者-护士口头沟通的认知见解
Maryam Zolnoori1,2,3, Ali Zolnour1, Sasha Vergez3
1Columbia University Irving Medical Center, New York, NY 10032, United States.
Journal of the American Medical Informatics Association : JAMIA
|December 12, 2024
概括
一个分析患者护士对话和电子健康记录 (EHR) 的AI工具可以有效地选早期认知衰退. 将语音分析与EHR数据相结合,可以显著提高像痴呆症这样的疾病的检测准确度.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
- 语音分析用于医学诊断
背景情况:
- 轻度认知障碍和痴呆症的诊断不足,导致医疗保健成本增加.
- 家庭医疗保健机构提供了通过患者护士互动进行早期检测的机会.
研究的目的:
- 开发一个语音处理算法,用于早期认知衰退的检测.
- 通过患者-护士口头沟通和电子健康记录 (EHR) 数据来评估算法的性能.
主要方法:
- 分析了来自47名患者的125条患者与护士沟通的音频录音.
- 开发了一种自然语言处理算法,以提取语言和交互特征.
- 使用F1分数和AUC指标,与基于EHR的方法比较算法性能.
主要成果:
- 仅仅通过语音分析,F1得分为85,AUC为86.47.
- 仅用EHR数据就实现了75.56的F1得分和79.56的AUC.
- 结合语音和EHR数据,获得了最高的性能 (F1得分88.89,AUC90.23).
- 语言指标包括减少多样性,语法问题和改变的语音模式.
结论:
- 常规的患者护士沟通包含认知障碍的重要指标.
- 将音频数据与EHR集成为早期认知衰退检测提供了更准确的方法.
- 这种方法有可能通过及时干预家庭医疗保健来改善患者的治疗结果.
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