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预测尼日利亚艾滋病毒感染者治疗中断:机器学习方法

Matthew-David Ogbechie¹, Christa Fischer Walker², Mu-Tien Lee³

¹FHI 360, Abuja, Nigeria.

|June 14, 2024

概括

机器学习模型可以预测艾滋病毒治疗中断,从而实现有针对性的干预. 这种方法通过在停止抗逆转录病毒治疗 (ART) 之前识别有风险的人来改善患者的治疗结果和成本效益.

科学领域:

公共卫生公共卫生
医疗信息学医疗信息学
机器学习在医学中的应用

背景情况:

抗逆转录病毒疗法 (ART) 将艾滋病毒管理转向了慢性疾病模型.
由于治疗中断率很高,因此需要有效的坚持和重新投入的战略.
目前针对ART坚持和重新参与的干预措施通常资源密集,可能是不可持续的.

研究的目的:

开发和整合一种机器学习 (ML) 模型,用于预测尼日利亚新注册的ART患者的30天治疗中断 (IIT).
评估卫生工作者对ML模型患者病例管理输出的看法和利用情况.

主要方法:

使用常规程序数据 (2005-2021) 训练并测试了ML模型 (增强树,极端梯度增强).
使用80/20数据分割进行培训和测试,并预先选择与IIT相关的变量.
将IIT定义为未能在预定后续检查日期后28天内补充ART.

主要成果:

分析包括136,747名客户;IIT费率从2017年之前的58.6%下降到2019年10月后的14.2%.
与IIT相关的因素包括入学时的疾病严重程度,怀孕,母乳养和设施特征.
选择的ML模型实现了81%的灵敏度,88%的特异性,83%的PPV和87%的NPV,并集成到电子医疗记录系统中.

结论:

基于常规数据,可以开发高性能ML模型来预测HIV IIT,并集成到健康管理信息系统中.
通过差异化护理模式,ML增强了干预的向性,在治疗中断之前提高了成本效益和患者的治疗结果.

关键词:

艾滋病病毒艾滋病病毒艾滋病病毒艾滋病毒计划艾滋病毒计划尼日利亚尼日利亚尼日利亚抗逆转录病毒药物治疗慢性疾病是一种慢性疾病.数据收集数据收集数据收集干预干预干预干预干预干预机器学习是机器学习.治疗中断治疗中断

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