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机器学习模型的应用,用于预测关节骨折手术后输血的可能性

Xiao Chen¹, Junpeng Pan², Yi Li¹

¹Department of Orthopaedics, Suzhou Hospital of Anhui Medical University, Suzhou, 234000, Anhui, China.

Aging clinical and experimental research

|September 21, 2023

概括

机器学习模型有效地预测了关节骨折手术后需要输血的需要. 这些模型利用了血红蛋白和年龄等因素,为管理贫血风险提供了有前途的临床应用.

科学领域:

计算医学是一种计算医学.
整形外科手术结果的结果
机器学习在医疗保健中的应用

背景情况:

贫血是部骨折手术后经常出现的并发症.
传统的治疗方法,即全基因输血,存在诸如败血症和发烧等风险.
有限的研究存在于机器学习预测输血需求在这个患者群体.

研究的目的:

开发和评估机器学习模型,用于预测关节骨折手术患者手术后输血的可能性.
确定影响输血需要的关键风险因素.

主要方法:

分析了1355名关节骨折手术患者,210人接受了输血.
使用十倍交叉验证开发了五种机器学习模型 (逻辑回归,MLP,XGBoost,RF,SVM).
用ROC曲线,AUC,MCC,校准图和决策曲线分析 (DCA) 来评估模型性能,并使用SHAP进行解释.

主要成果:

确定的主要预测因素包括血红蛋白 (HB),血球蛋白 (GLO),年龄,间接胆红素 (IBIL) 和显著的血液损失 (>200毫升).
模型在训练和测试组中都表现出强的表现,AUC值在0.7978到0.8598.8之间.
血红蛋白 (HB) 被确定为模型预测中最关键的因素.

结论:

开发的机器学习模型在预测关节骨折手术后输血需求方面表现强.
这些模型具有显著的临床应用潜力,帮助外科医生更好地评估风险和输血管理.
机器学习为优化患者护理和减少输血相关并发症提供了宝贵的工具.

关键词:

部骨折部骨折部骨折机器学习模型机器学习模型术后输血术后输血预测预测预测外科手术手术手术

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