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糖尿病足的生存预测:一种机器学习方法

Alina Delia Popa¹, Radu Sebastian Gavril¹, Iolanda Valentina Popa¹

¹Faculty of Medicine, University of Medicine and Pharmacy "Grigore T Popa", 700115 Iasi, Romania.

Journal of clinical medicine

|September 28, 2023

概括

本研究引入了第一个机器学习模型,用于预测糖尿病足 (DFU) 患者的5年和10年死亡风险. 这些模型利用非侵入性预测因素,为DFU患者提供准确的长期生存预测.

科学领域:

医疗信息学医疗信息学
医疗保健中的机器学习
糖尿病学糖尿病学

背景情况:

糖尿病足 (DFU) 是致病率和死亡率的重要原因.
预测DFU患者的长期死亡率对于临床管理至关重要.
现有的预测方法可能缺乏准确性或依赖于侵入性措施.

研究的目的:

开发和验证机器学习模型,用于预测DFU患者的5年和10年死亡率.
确定这一群体中长期死亡率的关键非侵入性预测因素.

主要方法:

使用635名DFU患者 (2007-2017) 的数据开发了两个多层感知子 (MLP) 分类器.
模型根据临床和人口因素预测5年和10年死亡率.
预测因素包括年龄,DFU严重性得分 (UT,瓦格纳-梅吉特,圣埃利安),GFR,病变特征和并发症 (缺血症,心血管疾病,病,高血压).

主要成果:

5年死亡率模型的准确度为0.7717 (训练) 和0.7244 (测试).
10年死亡率模型的准确度为0.7598 (训练) 和0.7087 (测试).
这些模型使用可访问,非侵入性数据展示了良好的预测性能.

结论:

机器学习模型可以准确预测DFU患者的长期死亡率.
非侵入性和低成本的预测器对风险分层是有效的.
这些模型为改善患者护理和DFU管理结果提供了有价值的工具.

关键词:

圣埃利安伤口评分系统德克萨斯大学的舞台系统.瓦格纳梅吉特的分类方式糖尿病人的足部.机器学习是机器学习.死亡率死亡率

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