开发一种可解释的机器学习模型,用于预测重症监护病房内脑内出血患者的静脉血栓栓塞
Menghui He1, Wenyan Liu1, Zhongsheng Lu2
1Department of Graduate School, Qinghai University, Xining, China.
Frontiers in neurology
|January 23, 2026
概括
这项研究开发了一种可解释的机器学习模型,用于预测在重症监护室 (ICU) 患有脑内出血 (ICH) 患者的静脉血栓栓塞 (VTE) 风险. 该模型准确地识别高风险患者,以便及时预防VTE.
科学领域:
- 关键护理医学 关键护理医学
- 神经学 神经学
- 数据科学数据科学数据科学
背景情况:
- 静脉血栓栓塞 (VTE) 是重症监护室 (ICU) 患有脑内出血 (ICH) 患者的重大风险.
- 这些患者的预防性抗凝剂仍然是一个有争议的临床决定.
- 准确的VTE风险预测对于指导预防策略至关重要.
研究的目的:
- 开发和验证可解释的机器学习 (ML) 模型,用于预测严重病情的ICH患者的VTE风险.
- 为了实现个性化和及时的预防措施,对VTE.
主要方法:
- 来自MIMIC-IV和青海省人民医院ICU的临床数据的回顾性分析.
- 使用最小绝对收缩和选择运算符 (LASSO) 进行特征选择.
- 开发了一个XGBoost模型,并使用AUC,DCA,精度,PPV和NPV验证其性能,并使用SHapley添加式扩展 (SHAP) 进行解释.
主要成果:
- XGBoost模型表现出强大的预测性能,AUC为0.936 (训练),0.778 (测试) 和0.761 (外部验证).
- 静脉动脉突发症的关键预测因素包括ICU停留时间,年龄,前列血时间,甘油三,白蛋白,BMI,PTT,血糖,白细胞计数和静脉血压.
结论:
- 开发的XGBoost模型准确地预测了ICU患有ICH的ICU患者的VTE.
- SHAP方法提供了对个体风险因素的洞察,支持个性化VTE预防和风险分层.
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