Real‐time electronic adherence monitoring (EAM) systems could inform on‐going risk assessment for HIV viraemia and be used to personalize viral load testing schedules. We evaluated the potential of real‐time EAM (transferred via cellular signal) and standard EAM (downloaded via USB cable) in rural Uganda to inform individually differentiated viral load testing strategies by applying machine learning approaches.

中文翻译：

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超级学习者分析约束优化下实时电子监测的抗逆转录病毒治疗依从性，并与乌干达农村艾滋病毒感染者的非差异化护理方法进行比较


实时电子依从性监测 (EAM) 系统可以为 HIV 病毒血症的持续风险评估提供信息，并用于个性化病毒载量检测计划。我们评估了乌干达农村地区实时 EAM（通过蜂窝信号传输）和标准 EAM（通过 USB 线下载）的潜力，通过应用机器学习方法为个体差异化病毒载量测试策略提供信息。