预测老年患者深静脉血栓的风险:七种机器学习模型的比较分析
Jianjie Ju1, Jingjing Chen1, Jingting Wang2
1The School of Pharmacy, Fujian Medical University, Fuzhou, Fujian, China.
概括
机器学习模型可以准确预测老年患者的深静脉血栓形成风险. 关键预测因素包括胀,疼痛,白蛋白和D-二次体,有助于临床决策.
科学领域:
- 心血管医学
- 医疗信息学
- 老年病学
背景情况:
- 深静脉血栓是老年人死亡的一个重要原因.
- 目前的风险评估工具对于这一群体来说是不够的.
- 在老年人中,DVT的发病率正在上升.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测老年患者的DVT风险.
- 确定该群体内DVT的主要风险因素.
- 创建一个临床决策支持工具,用于DVT风险分层.
主要方法:
- 对1226名心血管手术的老年患者进行了回顾性分析.
- 使用最小绝对收缩和选择运营商 (LASSO) 来识别风险因素.
- 通过网格搜索和交叉验证,训练并验证了七个ML模型,包括物流回归 (LR) 和随机森林 (RF).
主要成果:
- 后勤回归 (LR) 和随机森林 (RF) 模型显示出优异的性能.
- 实现的曲线下的面积 (AUC) 为LR的0.835和RF的0.819.
- 沙普利添加剂测试 (SHAP) 确定了胀,疼痛,白蛋白 (ALB) 和D-二次体作为关键预测因素.
结论:
- 开发的ML模型为老年患者提供了准确的DVT风险分层.
- 发现的关键预测因素可以提高临床评估.
- 一个互动的网络工具可以支持预防DVT的临床决策.
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