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一个分类模型的生成和验证,以诊断成年人的家族高胆固醇血症

João Albuquerque¹, Ana Margarida Medeiros², Ana Catarina Alves²

¹Departamento de Biomedicina, Unidade de Bioquímica, Faculdade de Medicina, Universidade do Porto, 4200-319, Porto, Portugal; Centro de Estatística e Aplicações, Faculdade de Ciências, Universidade de Lisboa, 1749-016, Lisboa, Portugal; Grupo de Investigação Cardiovascular, Departamento de Promoção da Saúde e Prevenção de Doenças Não Transmissíveis, Instituto Nacional de Saúde Doutor Ricardo Jorge, 1649-016, Lisboa, Portugal.

Atherosclerosis

|October 9, 2023

概括

早期诊断出家族性高胆固醇血症 (FH),可以降低心血管疾病的风险. 一个新的后勤回归模型,训练在多个队列,准确地识别FH病例跨多种人群,超越传统的标准.

科学领域:

心血管疾病的研究研究.
医学诊断医学诊断医学诊断
机器学习在医疗保健中的应用

背景情况:

家庭高胆固醇血症 (FH) 的早期诊断显著降低了心血管疾病 (CVD) 的风险.
目前的查方法通常依赖于单一队列研究,限制了概括性.
后勤回归 (LR) 和机器学习显示出FH查的前景.

研究的目的:

开发和验证基于逻辑回归 (LR) 的算法,用于家族高胆固醇血症 (FH) 查.
在多个国家队列和外部数据集中评估算法的性能.
将LR模型的疗效与传统的临床标准进行比较.

主要方法:

开发了一个物流回归 (LR) 算法,使用来自三个国家FH队列 (葡萄牙,巴西,瑞典) 的数据.
在这些队列和外部意大利数据集的独立样本上验证了LR模型.
使用AUROC和AUPRC评估歧视能力;与荷兰脂质临床网络 (DLCN) 标准进行性能比较.

主要成果:

与训练集相比,LR模型在测试集上显示出更高的AUROC和AUPRC值.
在巴西,瑞典和意大利测试集中,LR模型与DLCN标准相比,可以实现更准确的分类.
在使用LR模型的所有测试组中观察到更好的准确性,G平均值和F1得分.

结论:

与DLCN标准相比,LR模型表现出优越的分类能力,识别出类似数量的FH病例,错误阳性较少.
该模型在多种不同人群中显示出优异的概括性,表明其作为有效的FH查工具的潜力.
这种多队列开发的算法为广泛的FH查提供了强大的方法.

关键词:

荷兰脂质诊所网络标准荷兰脂质诊所网络标准家庭性高胆固醇血症 (FAMI) 是一种家族性高胆固醇血症.后勤回归的逻辑回归验证验证验证验证

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