开发和验证机器学习算法使用临床页面来预测即将发生的临床恶化.
Bryan D Steitz1, Allison B McCoy2, Thomas J Reese2
1Department of Biomedical Informatics, Vanderbilt University Medical Center, 2525 West End Ave., Suite 1475, Nashville, TN, 37203, USA. Bryan.d.steitz@vumc.org.
Journal of general internal medicine
|August 1, 2023
概括
机器学习分析临床页面消息,准确预测患者病情恶化,改善早期检测. 这种方法可以提高患者的安全性,而不会改变临床工作流程.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 对住院患者来说,早期发现临床恶化至关重要.
- 当前的自动化方法难以识别即将发生的关键事件.
研究的目的:
- 开发一种机器学习算法,用于预测即将发生的临床恶化.
- 使用临床呼叫机消息来提高预测准确度.
主要方法:
- 使用长期短期记忆 (LSTM) 机器学习模型.
- 从一项大型观察性研究中分析了临床呼叫机消息的内容和频率.
- 包括2018年至2020年期间超过8.7万例住院治疗.
主要成果:
- 该模型确定了62%的恶化事件发生在3小时内,47%发生在12小时内.
- 在AUC和灵敏度等关键指标上表现优于现有的早期预警分数.
- 在6小时达到0.856的AUC,超过了最佳早期预警得分 (0.781).
结论:
- 在临床页面数据上的机器学习显著改善了对即将发生的患者病情恶化的预测.
- 这种方法提供了增强的检测,而不会破坏临床工作流程或文档实践.
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