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评估机器学习模型的预测性能,有效性和适用性,以预测HIV治疗中断:系统性审查

Williams Kwarah^1,2, Frances Baaba da-Costa Vroom³, Duah Dwomoh³

¹Department of Biostatistics, School of Public Health, University of Ghana, Accra, Ghana. Kwarah@gmail.com.

BMC global and public health

|July 24, 2025

概括

机器学习模型在预测艾滋病毒治疗中断方面表现有前途. 然而,大多数研究都有很高的偏差风险,这凸显了在未来研究中需要更好的验证和数据处理.

科学领域:

人工智能在公共卫生中的作用
传染病预测模型的预测模型.

背景情况:

艾滋病毒治疗中断是全球艾滋病毒/艾滋病控制的主要障碍.
机器学习 (ML) 提供了利用临床数据预测治疗中断的潜力.
了解ML模型开发,验证和应用对于研究进步至关重要.

研究的目的:

系统地审查用于预测HIV治疗中断的机器学习模型.
评估这些模型的开发,验证,性能和偏差风险.

主要方法:

在多个数据库 (PubMed,Scopus等) 进行了全面的文献搜索. 从1990年开始到2024年9月.
研究被选,并使用CHARMS检查清单提取数据.
使用PROBAST评估了偏差风险,并遵循PRISMA指南.

主要成果:

九项研究报告了12个ML模型,主要是随机森林,XGBoost和AdaBoost.
所有模型都经过了内部验证,但只有两个有外部验证.
模型显示中度歧视 (AUC-ROC平均值=0.668),其中75%显示因数据处理和缺乏校准/DCA而存在偏差的高风险.

结论:

机器学习模型显示出预测HIV治疗中断的潜力,特别是在资源有限的环境中.
未来的研究必须侧重于外部验证,强大的缺失数据处理和决策曲线分析.
纳入社会文化预测因素可以提高模型的稳定性和临床效用.

关键词:

艾滋病毒治疗中断治疗中断机器学习机器学习预测建模的预测建模.

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