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在使用XGBoost机器学习的老年阿司匹林使用者内部出血的预测模型.

Tenggao Chen1, Wanlin Lei2, Maofeng Wang2

  • 1Department of Colorectal Surgery, Affiliated Dongyang Hospital, Wenzhou Medical University, Dongyang, Zhejiang, 322100, People's Republic of China.

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概括

这项研究开发了一种机器学习模型,用于预测老年阿司匹林使用者内部出血风险. 该模型使用六个临床变量,帮助临床医生管理患者的治疗和护理.

关键词:
阿司匹林是一种阿司匹林.流血 出血 流血 出血极端的梯度增强了极端的梯度.发生出血 发生出血这个名字是名ogramogram.预测模型是一个预测模型.

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科学领域:

  • 医疗信息学 医疗信息学
  • 老年学是指老年学的学科.
  • 心血管医学 心血管医学

背景情况:

  • 对于老年人来说,阿司匹林是广泛的处方,增加了胃肠道出血的风险.
  • 需要预测模型来识别高风险患者,以便主动管理.

研究的目的:

  • 开发和验证基于机器学习的预测模型,用于老年阿司匹林使用者的内出血风险.
  • 确定与该人群中出血事件相关的关键临床因素.

主要方法:

  • 对26,030名老年阿司匹林使用者 (65岁以上) 的回顾性分析.
  • 使用最小绝对收缩和选择运算符 (LASSO) 回归,极端梯度提升 (XGBoost) 和多变量后勤回归的预测模型的开发.
  • 使用曲线下的面积 (AUC),校准曲线和决策曲线分析 (DCA) 评估模型性能.

主要成果:

  • XGBoost模型表现出卓越的性能,AUC为0.842 (训练) 和0.820 (测试).
  • 确定的主要预测因素包括血红蛋白 (HGB),血小板数 (PLT),以前的出血史,胃,脑梗塞和瘤.
  • 基于这些六个变量,开发了一个名图,显示出良好的区分和校准性能.

结论:

  • 成功开发了一种针对老年阿司匹林使用者出血风险的强大预测模型.
  • 模型和相关的名图可以帮助临床医生进行风险分层和个性化治疗决策.