一个深度学习框架来预测患者在体外膜氧化装置上的脱化:开发和模型分析研究研究
Joshua Fuller1, Alexey Abramov2, Dana Mullin3
1Vagelos College of Physicians and Surgeons, Columbia University, New York City, NY, United States.
JMIR biomedical engineering
|June 14, 2024
概括
一个深度学习模型,CEVVO,有助于预测有毒体外膜氧化 (VV-ECMO) 患者的成功脱. 它将患者分为高风险和低风险组,帮助临床决定断奶试验.
科学领域:
- 人工智能在医学中的应用
- 关键护理医学 关键护理医学
- 机器学习应用 机器学习应用
背景情况:
- 静脉外体膜氧化 (VV-ECMO) 对耐火性呼吸衰竭至关重要.
- 脱决定依赖于断奶试验和临床判断,缺乏可靠的预后指标.
- 现有有限的工具可以预测VV-ECMO的成功解.
研究的目的:
- 开发和验证VV-ECMO结果的持续评估 (CEVVO) 模型.
- CEVVO的目标是利用深度学习来预测VV-ECMO患者的脱管成功.
- 为管理VV-ECMO患者的临床医生提供日常风险分层工具.
主要方法:
- 利用长期短期记忆网络整合118名患者的离散和连续ECMMO数据.
- 通过五倍交叉验证,使用接收器操作特征曲线 (AUROC) 下面面积和平均精度 (AP) 评估模型性能.
- 校准和分层预测到风险组 (0=高风险,3=低风险) 并在合成数据集上验证.
主要成果:
- 与当代模型相比,CEVVO的分类性能优越 (P<.001).
- 风险分类系统显示出显著的潜力:72小时脱率为58% (高风险) 和92% (低风险;P=.04).
- 96小时的脱率为54% (高风险) 和100% (低风险;P=.01),突出了CEVVO的预测能力.
结论:
- 对VV-ECMO患者进行准确的风险分层需要使用先进模型整合大型数据集.
- CEVVO框架显示了加强重症监测系统的前景.
- 这个模型可以帮助临床医生做出有关VV-ECMO脱管的明智决策.
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