开发和验证一种可解释的预测模型,用于在胃潰瘍内镜血静后的短期再出血:一项回顾性单中心队列研究
Run-Hua Li1, Ying Zhu2, Wen Xu3
1Department of Gastroenterology, Southern Medical University (Shenzhen School of Clinical Medicine, Southern Medical University), Shenzhen, Guangdong, People's Republic of China.
Surgical endoscopy
|December 30, 2025
概括
一个新的可解释的机器学习模型准确地预测了患有胃潰瘍出血 (PUB) 的患者的再出血风险. 该工具有助于识别高风险个人进行个性化治疗,改善结果并降低医疗保健成本.
科学领域:
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
- 临床决策支持 临床决策支持
背景情况:
- 胃潰瘍出血 (PUB) 是死亡率和發病率的重要原因.
- 目前的PUB复出风险预测工具缺乏准确性和可解释性.
- 内镜治疗虽然对初始静血有效,但在预防短期再出血方面存在局限性.
研究的目的:
- 开发和验证一种可解释的机器学习 (ML) 模型,用于预测PUB患者内镜治疗后短期再出血风险.
- 与现有方法相比,提高风险预测的准确性和可解释性.
- 为早期识别高风险患者提供一个工具,以指导个性化管理.
主要方法:
- 对204名患有Forrest Ia-IIb PUB的患者进行了回顾性分析,这些患者正在接受紧急血液静止.
- 评估了11个ML算法,数据分为训练 (70%) 和测试 (30%) 集.
- 使用AUC,灵敏度,特异性,精度,F1得分和尤登指数进行性能评估;通过夏普利添加式解释 (SHAP) 进行解释.
主要成果:
- 分类提升 (CATBoost) 模型表现出卓越的性能,AUC为0.980 (训练) 和0.817 (测试).
- SHAP分析确定了关键预测因子,包括罗卡尔得分,前血时间 (PT),激活的部分血栓形成时间 (APTT),AIMS65得分,BUN,肌素和病变位置.
- 开发了一个稳定,精确校准的模型,从而开发了一个基于网络的计算器,用于个性化风险估计.
结论:
- 一个可解释的CATBoost ML模型已成功开发和验证,用于预测PUB的短期再出血.
- 该模型有效地利用常规的临床和实验室变量来进行风险分层.
- 该工具有可能在早期识别高风险患者方面发挥重要作用,促进个性化管理策略,并可能减少死亡率和医疗负担.
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