自动机器学习用于急性胰腺炎系统性炎症反应综合征的早期预测
Rufa Zhang1, Shiqi Zhu2, Li Shi3
1Department of Gastroenterology, The People's Hospital of Nanchuan, No. 16, Nanda Street, Nanchuan District, Chongqing, 408400, China.
BMC medical informatics and decision making
|April 17, 2025
概括
自动机器学习 (AutoML) 能够有效地预测急性胰腺炎 (AP) 中的全身炎症反应综合征 (SIRS). 这种深度学习模型为AP患者早期SIRS检测提供了一个临床上重要的工具.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 临床预测建模临床预测建模
背景情况:
- 系统性炎症反应综合征 (SIRS) 是急性胰腺炎 (AP) 的重要并发症,有助于增加死亡率.
- 在AP中早期和准确预测SIRS对于及时干预和改善患者结果至关重要.
研究的目的:
- 开发和验证一种自动机器学习 (AutoML) 模型,用于急性胰腺炎患者早期和精确预测SIRS.
- 将AutoML模型的性能与用于SIRS预测的传统后勤回归进行比较.
主要方法:
- 来自多个中心的1,224名AP患者的回顾性分析 (2017-2021年).
- 使用 LASSO,AutoML (包括深度学习) 和多变量逻辑回归的预测模型的开发和验证.
- 使用ROC曲线,校准曲线和决策曲线分析 (DCA) 的性能评估.
- 通过特征重要性,SHAP和LIME评估可解释性.
主要成果:
- 自动ML模型,特别是深度学习 (DL) 模型,与逻辑回归相比,表现出更高的性能.
- DL模型在ROC曲线下的面积达到0.843 (培训),0.848 (验证) 和0.867 (测试).
- 该研究证实了AutoML衍生DL模型的临床意义和可解释性.
结论:
- 使用深度学习算法开发的AutoML模型显示,在急性胰腺炎中早期预测SIRS具有显著的临床实用性.
- 自动ML提供了一种强大的方法,可以提高临床决策的准确性和及时性.
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