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在患有部骨折的老年患者中,构建和评估术后再骨折的预测模型

Jingjing Wu¹, Qingqing Zeng², Sijie Gui³

¹School of Nursing, Hengyang Medical School, University of South China, Hengyang, China; School-Enterprise Cooperative Innovation and Entrepreneurship Education Base, University of South China-Hunan Lantern Medical Technology Co., Ltd, Hengyang, China.

International journal of medical informatics

|December 7, 2024

概括

这项研究开发了一种自动机器学习模型,用于预测老年关节骨折患者的再骨折风险. 权重Ensemble_L2模型表现出高准确度,有助于早期识别高风险个体.

科学领域:

老年医学老年医学
整形外科手术整形外科手术
医疗保健中的人工智能

背景情况:

高龄的关节骨折患者面临着术后复发骨折的重大风险.
早期识别高风险个体对于有效的管理和干预至关重要.

研究的目的:

开发和验证一种自动机器学习 (ML) 模型,用于预测老年关节骨折患者的术后再骨折.
确定与再骨折相关的独立风险因素.
为早期风险评估和患者管理提供一个工具.

主要方法:

分析了来自老年关节骨折患者的临床数据,以确定独立的风险因素.
使用自动化ML算法 (AutoGluon) 来构建各种预测模型.
模型对70%的数据进行了训练,对30%进行了验证,并进行了外部验证.
模型的性能使用诸如曲线下的面积 (AUC) 等指标进行评估.

主要成果:

手术后再骨折的发生率约为11.7%.
确定的独立风险因素包括年龄,糖尿病,骨质疏松症,康复运动状态和手术前的总蛋白质水平.
权重Ensemble_L2模型实现了最高的预测性能,在外部验证中AUC为0.9759.

结论:

开发的WeightedEnsemble_L2模型是医疗保健专业人员的高度通用和适用的工具.
这种模型可以有效地评估和管理老年关节骨折患者的再骨折风险.
自动化ML为预测骨科手术中不良结果提供了一种有希望的方法.

关键词:

自动机器学习自动机器学习部骨折部骨折部骨折预测模型的预测模型.再次骨折的发生.

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