开发一个动态预测模型,用于计划外的ICU入院和住院患者的死亡率
Davide Placido1, Hans-Christian Thorsen-Meyer1,2, Benjamin Skov Kaas-Hansen2,3
1Novo Nordisk Foundation Center for Protein Research, University of Copenhagen, Denmark.
PLOS digital health
|June 9, 2023
概括
使用电子健康记录的深度学习模型可以预测患者的病情恶化,包括计划外的重症监护室 (ICU) 转移和医院死亡. 这种先进的风险评估工具为更好的患者管理提供了宝贵的临床见解.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床风险预测预测
背景情况:
- 准确的患者严重程度评估对于预防不良结果至关重要,例如医院死亡率和计划外的重症监护室 (ICU) 转移.
- 传统的严重程度得分通常依赖于患者的有限特征,可能缺少健康状况的动态变化.
- 深度学习模型通过整合异质数据源进行动态预测,提供了增强的个性化风险评估.
研究的目的:
- 评估深度学习方法在从电子健康记录中捕获纵向健康状况变化的能力.
- 开发和评估一个深度学习模型,用于预测计划外的ICU转移和医院死亡的复合结果.
- 为临床医生提供可操作的洞察力,了解患者临床恶化风险因素.
主要方法:
- 开发一种深度学习模型,利用来自多个数据源和循环神经网络的嵌入式文本.
- 定期预测患者的风险,使用时间标记的电子健康记录数据,包括病史,生化测量和临床笔记.
- 使用Shapley算法识别特征对风险预测的贡献的模型解释.
主要成果:
- 性能最好的深度学习模型整合了所有数据模式,实现了接收器运行特征曲线下的面积为0.898,具有6小时的评估率和14天的预测窗口.
- 该模型表现出强烈的歧视和校准,表明其作为临床支持工具的潜力.
- 沙普利算法为影响风险预测的可采取行动和不可采取行动的患者特征提供了洞察力.
结论:
- 深度学习模型可以有效地从电子健康记录中捕捉患者健康状况的纵向变化,从而进行动态风险预测.
- 开发的模型作为一个可行的临床支持工具,用于早期检测患有恶化高风险的患者.
- 该模型提供特征特定洞察力的能力增强了患者管理和干预的临床决策.
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