基于机器学习的消化系统瘤患者深静脉血栓的临床预测模型的构建和验证
Yunfeng Zhang1, Yongqi Ma2, Jie Wang1
1Department of Vascular Surgery, Shanxi Provincial People's Hospital (The Fifth Clinical Medical School of Shanxi Medical University) No. 29 Shuangtasi Street, Taiyuan 030012, Shanxi, China.
American journal of cancer research
|February 7, 2024
概括
这项研究开发了一种XGBoost机器学习模型,用于预测患有消化系统瘤的手术患者的深静脉血栓形成 (DVT) 风险. 该模型准确识别高风险个体,有助于预防和治疗DVT.
科学领域:
- 医疗信息学 医疗信息学
- 在瘤学瘤学.
- 血管外科 血管外科
背景情况:
- 深静脉血栓症 (DVT) 是在接受消化系统瘤手术的患者的重大并发症.
- 准确的风险预测对于有效的DVT预防和管理至关重要.
研究的目的:
- 开发和验证基于机器学习的模型,用于预测在接受手术的消化系统瘤患者中DVT风险.
- 在这个患者群体中确定DVT的关键预测因子.
主要方法:
- 对845名患有消化系统瘤的患者的回顾性分析.
- 使用六种机器学习模型进行特征选择:拉索回归,XGBoost,随机森林,决策树,支持矢量机器和物流.
- 模型验证使用接收器操作特征 (ROC) 曲线,精度回忆曲线和决策曲线分析.
主要成果:
- XGBoost模型显示出优异的预测性能,在验证集中,曲线下的最大面积 (AUC) 是 (P < 0.05).
- 确定的主要预测因素包括身体质量指数 (BMI),手术时间和D-二次数水平.
- 开发的模型有效地区分了高风险和低风险的DVT患者.
结论:
- XGBoost模型是评估消化系统瘤手术患者DVT风险的有效工具.
- 该模型的临床实施可以增强DVT预防和治疗策略.
- 体重指数 (BMI),手术时间和D-二次数是影响该群体DVT风险的关键因素.
关键词:
拉索回归是一种回归方式.机器学习是机器学习.在XGBoost中使用.决策树是一个决策树.深静脉血栓形成的原因是深静脉血栓形成.消化系统的瘤.随机的森林随机的森林风险预测风险预测支持矢量机器的支持矢量机器更多相关视频
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