基于机器学习的急性胰腺炎相关的肺损伤预测模型:回顾性分析
Zhaohui Du1, Qiaoling Ying2, Yisen Yang3
1Department of Emergency Surgery, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China.
Frontiers in medicine
|August 28, 2025
概括
机器学习模型可以准确预测急性胰腺炎相关的肺损伤 (APALI). 开发的XGBoost和随机森林模型确定了关键的风险因素,使得这种严重的并发症能够得到早期干预.
科学领域:
- 医疗信息学
- 危急护理医学
- 在医疗保健中的机器学习
背景情况:
- 急性胰腺炎相关的肺损伤 (APALI) 是急性胰腺炎的严重并发症,与高发病率和死亡率有关.
- 早期诊断和干预对于改善患者的结果至关重要.
研究的目的:
- 开发和验证用于预测APALI的机器学习模型.
- 确定与APALI发展相关的关键临床因素.
主要方法:
- 采用了两家主要医院 (2012-2023) 的数据,共计1975名患者.
- 应用LASSO回归来选择特征并构建六个机器学习模型.
- 评估模型使用AUC,精度,灵敏度,特异性,F1分数和回忆,并使用SHAP进行特征重要性分析.
主要成果:
- XGBoost和随机森林模型显示出优异的预测性能 (AUC分别为0. 92和0. 914).
- 确定的主要预测因素包括CRP,BMI,中性粒细胞数量,,乳酸和中性粒细胞与白蛋白的比率 (NAR).
- 根据最佳模型开发了一个在线风险计算器.
结论:
- 已开发和验证的机器学习模型显示了APALI的强大预测能力.
- 在线风险计算器可以帮助早期风险分层和临床决策.
- 建议进一步进行前性多中心验证以确认可通用性.
相关概念视频
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Assessment:
Assessment:
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