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人工智能方法用于预测骨质疏松性骨折的应用

Dorota Lis-Studniarska¹, Marta Lipnicka², Marcin Studniarski²

¹Central Clinical Hospital, Medical University of Łódź, Pomorska 251, 92-213 Łódź, Poland.

Life (Basel, Switzerland)

|August 26, 2023

概括

骨质疏松症的危险因素,如年龄和慢性病,显著影响低能量的骨折. 机器学习模型可以帮助识别患有早期干预风险的患者.

科学领域:

老年学与公共卫生
生物医学工程生物医学工程
数据科学在医学中的数据科学

背景情况:

骨质疏松症在老龄化人口中构成了重大的社会经济挑战,骨折造成的治疗成本很高.
骨质疏松性骨折导致慢性残疾,并发症,降低生活质量和增加死亡率.
识别关键风险因素对于治疗骨质疏松症和预防骨折至关重要.

研究的目的:

确定患者特异性低能量骨折风险因素的影响等级.
开发一种预测模型,用于识别易受低能断裂影响的个体.

主要方法:

追溯分析222名患者的医疗文件 (206名女性,16名男性).
使用人工神经网络,物流回归,k-最近邻居和支持矢量机 (SVM) 来进行分类.
使用递归特征消除来排名风险因素.

主要成果:

后勤回归确定年龄,慢性病,部T分数和血清酸盐是最重要的危险因素.
在这些四个因素上训练的神经网络模型在测试数据上实现了平均平均平方误差约27%.
风险因素的排名表明,随着新患者的增加,对数据变化的敏感性.

结论:

该研究强调了低能量骨折的关键预测因素,有助于患者风险识别.
预测模型的灵敏度表明需要进行更大的队列研究和先进的机器学习技术.
这些发现可以为患有骨质疏松性骨折高风险的个体提供早期,全面的治疗策略.

关键词:

人工神经网络的人工神经网络骨折骨折骨折骨折在逻辑回归的逻辑回归方法医疗记录医疗记录骨质疏松症是一种骨质疏松症.有关风险因素的风险因素.

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