Center for Data Science and Public Policy, Department of Computer Science, University of Chicago, Chicago, United States.
Chicago Center for HIV Elimination, Department of Medicine, University of Chicago, Chicago, United States.
Sci Rep. 2020 Apr 14;10(1):6421. doi: 10.1038/s41598-020-62729-x.
Consistent medical care among people living with HIV is essential for both individual and public health. HIV-positive individuals who are 'retained in care' are more likely to be prescribed antiretroviral medication and achieve HIV viral suppression, effectively eliminating the risk of transmitting HIV to others. However, in the United States, less than half of HIV-positive individuals are retained in care. Interventions to improve retention in care are resource intensive, and there is currently no systematic way to identify patients at risk for falling out of care who would benefit from these interventions. We developed a machine learning model to identify patients at risk for dropping out of care in an urban HIV care clinic using electronic medical records and geospatial data. The machine learning model has a mean positive predictive value of 34.6% [SD: 0.15] for flagging the top 10% highest risk patients as needing interventions, performing better than the previous state-of-the-art logistic regression model (PPV of 17% [SD: 0.06]) and the baseline rate of 11.1% [SD: 0.02]. Machine learning methods can improve the prediction ability in HIV care clinics to proactively identify patients at risk for not returning to medical care.
在 HIV 感染者中保持一致的医疗照护对于个人和公共健康都至关重要。“留存在医疗照护中”的 HIV 阳性个体更有可能被开处抗逆转录病毒药物并实现 HIV 病毒抑制，从而有效消除将 HIV 传播给他人的风险。然而，在美国，不到一半的 HIV 阳性个体留存在医疗照护中。改善留观的干预措施需要大量资源，目前还没有系统的方法来识别有脱离医疗照护风险、可能从这些干预措施中受益的患者。我们开发了一种机器学习模型，利用电子病历和地理空间数据来识别城市 HIV 护理诊所中可能脱离医疗照护的患者。该机器学习模型在标记前 10%最高风险患者需要干预时的阳性预测值为 34.6%（SD：0.15），优于之前的最先进的逻辑回归模型（PPV 为 17%（SD：0.06%））和基线率 11.1%（SD：0.02%）。机器学习方法可以提高 HIV 护理诊所的预测能力，主动识别有不返回医疗护理风险的患者。
