利用机器学习模型来预测急性胰腺炎的结果:在三个回顾性队列中进行开发和验证
1Department of Internal Medicine, Shaoxing Maternity and Child Health Care Hospital, Shaoxing, Zhejiang, China.
BMC medical informatics and decision making
|July 11, 2025
概括
机器学习模型,特别是XGBoost,可以预测急性胰腺炎 (AP) 患者的结果,包括死亡率和再录取. 这项技术有助于临床医生确定短期和长期预后,以更好地管理患者.
科学领域:
- 计算医学和数据科学
- 临床信息学和预测分析.
背景情况:
- 急性胰腺炎 (AP) 的再入院率很高,但缺乏强大的预测模型来预测出院后的结果.
- 对于AP患者而言,现有的医院预测模型存在重大局限性.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测AP患者的预后.
- 包括住院死亡率,再入院率和出院后死亡率.
- 将ML模型的预测性能与已建立的临床分数进行比较.
主要方法:
- 在三个数据库中对2,559名AP患者的临床和实验室数据进行了回顾性分析.
- 使用单变量逻辑回归和 LASSO 方法进行变量选择.
- 使用培训和外部验证集构建和验证六个ML算法,包括后勤回归,随机森林和XGBoost.
主要成果:
- 后勤回归,随机森林和XGBoost模型在预测死亡率方面表现出卓越的表现.
- XGBoost模型在预测ICU入院AP患者的住院死亡率方面表现最佳.
- XGBoost在各个中心表现出稳定性,平衡的灵敏度/特异性,以及有效的过拟合预防.
结论:
- 使用动态在医院变量的XGBoost模型可以有效地预测AP患者的短期和长期预后.
- 这种ML方法支持临床决策,以改善AP患者管理.
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