机器学习模型的开发和比较评估,用于预测胃肠癌患者的下肢深静脉血栓形成,使用多中心纵向临床数据
Frontiers in surgery
|October 27, 2025
概括
在胃肠道癌症患者中,深静脉血栓形成 (DVT) 风险可以使用机器学习模型预测. 这些模型识别了诸如手术和化疗等高风险因素,有助于个性化预防策略.
科学领域:
- 在瘤学瘤学.
- 血管医学 血管医学
- 在医疗保健中的数据科学.
背景情况:
- 深静脉血栓症 (DVT) 是胃肠道恶性瘤的一个重大并发症,影响患者的预后和生活质量.
- 准确的风险预测对于制定有效的预防和治疗策略至关重要.
研究的目的:
- 开发和验证机器学习模型,用于预测胃肠道恶性瘤患者的DVT风险.
- 在这个患者群体中确定DVT的独立预测因子.
主要方法:
- 对2020-2024年多中心纵向临床数据的回顾性分析.
- 使用单变量/多变量分析和四种机器学习算法 (XGBoost,RF,SVM,KNN) 进行DVT风险预测.
- 使用ROC曲线,校准图,布里尔分数,DCA,十倍交叉验证和独立的外部队列的验证模型. 为了解释性,使用了SHAP分析.
主要成果:
- 手术,长时间固定,中央静脉导管,放射治疗,远程转移和化疗被确定为显著的DVT危险因素.
- 所有机器学习模型都表现出强大的性能,XGBoost表现出卓越的歧视,校准和临床实用性.
- 外部验证证实了XGBoost模型的稳定性和通用性.
结论:
- 机器学习模型使用多中心临床数据有效预测胃肠道恶性瘤患者的DVT风险.
- 这些模型为个性化风险分层提供了有价值的工具,改善了预防策略和临床决策,以便更好地管理患者.
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