对用于预测静脉血栓栓塞风险的机器学习模型的系统审查
Wen-Jing Ge1, Teng-Fei Zhu1, Wen-Jie Ge2
1Department of Nursing, The First Affiliated Hospital of Bengbu Medical University, Bengbu, 233000, China.
Thrombosis research
|November 1, 2025
概括
机器学习 (ML) 模型对预测住院患者静脉血栓塞栓症 (VTE) 风险充满希望. 然而,目前的模型往往存在方法上的缺陷,缺乏外部验证,限制了它们的临床使用.
科学领域:
- 医疗信息学 医疗信息学
- 临床预测模型临床预测模型
- 医疗保健服务研究 医疗服务研究
背景情况:
- 静脉血栓塞栓症 (VTE) 对住院患者构成重大风险.
- 准确的风险预测对于有效的预防策略至关重要.
- 机器学习 (ML) 提供了改善VTE风险评估的潜力.
研究的目的:
- 系统地审查ML模型的方法质量,性能和临床适用性,用于在住院患者中预测VTE风险.
- 评估报告透明度和在开发VTE预测模型的研究中存在偏差风险.
- 确定现有 VTE ML 模型中使用的关键预测因素.
主要方法:
- 对多个数据库 (如PubMed,Embase,Web of Science) 的系统审查至2024年8月.
- 包括17项研究开发了65个VTE ML模型.
- 使用PROBAST和修改后的TRIPOD+AI框架评估模型质量.
主要成果:
- 所有65个VTE ML模型都实现了曲线下的面积 (AUC) >0.7.7.
- 后勤回归,XGBoost和随机森林是常见的算法.
- 大多数研究缺乏外部验证,这表明偏差风险很高,概括性有限.
结论:
- ML模型显示了对VTE风险预测的潜力.
- 在方法质量和外部验证方面存在重大局限性.
- 未来的研究需要大规模的,多中心的前性研究,以获得强大的临床工具.
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