标题： 用于保留患者在HIV护理中的机器学习系统 显示英文标题

标题： A Machine Learning System for Retaining Patients in HIV Care

摘要： 保留感染人类免疫缺陷病毒（PLWH）在医疗护理中对于防止病毒的新传播以及让PLWH拥有正常和健康的生活期限至关重要。 与HIV提供者保持定期预约并终身每天服用药物是非常困难的。 51%的PLWH未能遵循药物治疗并最终退出医疗护理。 目前重新将个人与护理联系起来的方法是被动的（在患者退出之后），因此效果不佳。 我们描述了我们的系统，用于预测谁最有可能退出护理，供芝加哥大学HIV诊所和芝加哥公共卫生部门使用。 模型的选择是基于其在资源限制下的预测性能、随时间的稳定性以及公平性。 我们的系统适用于临床环境中的护理点系统，以及支持城市层面常规干预的批量预测系统。 我们的模型在临床模型上的表现比基线好3倍，在全市范围模型上的表现比基线好2.3倍。 代码已发布在github上，我们希望这种方法，特别是我们对公平性的关注，能被其他诊所和公共卫生机构采用，以遏制艾滋病病毒流行。 显示英文摘要

摘要： Retaining persons living with HIV (PLWH) in medical care is paramount to preventing new transmissions of the virus and allowing PLWH to live normal and healthy lifespans. Maintaining regular appointments with an HIV provider and taking medication daily for a lifetime is exceedingly difficult. 51% of PLWH are non-adherent with their medications and eventually drop out of medical care. Current methods of re-linking individuals to care are reactive (after a patient has dropped-out) and hence not very effective. We describe our system to predict who is most at risk to drop-out-of-care for use by the University of Chicago HIV clinic and the Chicago Department of Public Health. Models were selected based on their predictive performance under resource constraints, stability over time, as well as fairness. Our system is applicable as a point-of-care system in a clinical setting as well as a batch prediction system to support regular interventions at the city level. Our model performs 3x better than the baseline for the clinical model and 2.3x better than baseline for the city-wide model. The code has been released on github and we hope this methodology, particularly our focus on fairness, will be adopted by other clinics and public health agencies in order to curb the HIV epidemic.