使用机器学习和深度学习模型预测高血压患者的死亡率
1Department of Electrical and Computer Engineering, Northwestern University, Evanston, IL, United States.
Frontiers in cardiovascular medicine
|August 27, 2025
概括
深度学习模型,特别是1D CNN, 准确地预测危急高血压患者的死亡率. 关键预测因素包括APS-III评分,年龄和ICU停留时间,改善了患者的预测结果.
科学领域:
- 危急护理医学
- 医疗信息学
- 在医疗保健中的机器学习
背景情况:
- 在重症监护室 (ICU) 预测高血压患者的死亡率对于临床决策至关重要.
- 传统的预测工具往往缺乏复杂的临床变量相互作用.
- 机器学习 (ML) 和深度学习 (DL) 为开发更准确的预测模型提供了先进的能力.
研究的目的:
- 评估各种ML和DL模型在危急高血压患者中预测死亡率的性能.
- 在该患者群体中确定临床死亡率的关键预测因素.
- 为了比较不同ML和DL模型的死亡率预测的有效性.
主要方法:
- 对30 096名危急高血压ICU患者的回顾性分析.
- 传统的ML模型 (逻辑回归,决策树,SVM) 与DL模型 (1D CNN,LSTM) 的比较.
- 使用接收器操作特征曲线下的面积 (AUC) 和SHapley添加式扩展 (SHAP) 进行预测.
主要成果:
- 1D卷积神经网络 (CNN) 模型实现了最高的AUC (0. 7744),超过了其他ML和DL模型.
- 在模型中发现的死亡率的关键预测因素包括急性生理和慢性健康评估III (APS-III) 评分,患者年龄和ICU停留时间.
- SHAP分析证实了这些预测因素对死亡风险评估的重大影响.
结论:
- 与传统的ML模型相比,深度学习模型,特别是1DCNN显示出高危高血压患者的死亡率预测精度更高.
- 将DL模型整合到临床工作流程中可以改善针对性干预的高风险患者的早期识别.
- 在临床实践中对DL模型实施的前性验证和伦理考虑需要进一步的研究.
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