构建和验证基于机器学习的预测模型,用于在脊髓手术后深静脉血栓形成
Xingyan Wu1, Zhao Wang1, Leilei Zheng1
1Department of Anesthesiology, Second Affiliated Hospital of Zunyi Medical University, Guizhou Province, China.
International journal of medical informatics
|September 11, 2024
概括
机器学习使用七个关键变量准确预测脊髓手术后的深静脉血栓栓塞 (DVT). 这种模型有助于临床医生识别有风险的患者并改善手术结果.
科学领域:
- 医疗信息学 医疗信息学
- 手术结果研究研究.
- 医疗保健中的机器学习
背景情况:
- 深静脉血栓塞栓症 (DVT) 是脊髓手术后的一种严重的术后并发症.
- 目前对脊椎外科手术后DVT的预防性抗凝剂策略缺乏最终的共识.
- 预测建模对于脊椎外科手术患者的主动DVT管理至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测脊椎手术后的DVT.
- 在这个患者群体中确定与DVT形成相关的关键风险因素.
- 加强对DVT预防策略的临床决策.
主要方法:
- 对500名接受选择性脊柱手术的患者的回顾性分析 (2020年1月-2022年12月).
- 使用Boruta-SHAP进行特征选择,使用SMOTE进行数据平衡.
- 开发和内部验证五个ML模型,并对150名患者进行外部验证.
主要成果:
- 七个变量被确定为关键预测因素:APTT,年龄,BMI,Crea,麻醉时间,罗库和普罗波剂量.
- 随机森林 (RF) 分类器在内部验证中显示出卓越的预测性能.
- 模型性能使用AUC,G-平均值,灵敏度,精度,特异性和F1评分进行评估.
结论:
- 成功开发了一种基于ML的预测模型,包含七个手术前和手术内变量.
- 已建立的模型为临床评估和决策支持提供了有价值的工具,用于预防脊椎后手术后的DVT.
- 这种预测方法可以帮助个性化风险评估和有针对性的干预措施.
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