在急性胰腺炎中可解释的ICU转移预测:神经网络模型具有SHAP可解释性,用于临床决策支持
IEEE transactions on bio-medical engineering
|September 23, 2025
概括
一个新的机器学习模型准确地预测了急性胰腺炎 (AP) 患者的重症监护室 (ICU) 入院情况. 这种工具有助于临床医生早期识别和治疗,改善患者的治疗结果.
科学领域:
- 医疗信息学 医疗信息学
- 临床预测模型临床预测模型
- 医疗保健中的机器学习
背景情况:
- 急性胰腺炎 (AP) 是一种严重的疾病,需要在某些患者的重症监护室 (ICU) 入院.
- 预测AP患者的ICU转移对于及时干预和资源分配至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测AP患者的ICU入院情况.
- 通过准确预测ICU需求,提高住院医疗保健.
主要方法:
- 使用了来自MIMIC-III,MIMIC-IV和eICU数据库的数据.
- 通过使用Boruta,Lasso和RFE算法与XGBoost识别了15个关键特性.
- 开发并比较了7个ML模型,包括神经网络 (NNET).
主要成果:
- 神经网络模型实现了最高的性能,AUC为0.891.1.
- 在试验组中,获得了0.852的灵敏度和0.767的特异性.
- SHAP分析提供了模型可解释性和特征重要性.
结论:
- 一个可解释的ML模型有效地预测了AP患者的ICU入院情况.
- 开发的模型是早期识别和治疗的可靠工具.
- 该模型可通过临床使用的网络平台访问.
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