List of data tables.

Nigeria, with the second-largest HIV epidemic globally, faces challenges in achieving its HIV epidemic control goals by 2030, with interruptions in treatment (IIT) a significant challenge. Machine learning (ML) models can help HIV programs implement targeted interventions to improve the quality of care, develop effective early interventions, and provide insights into optimal resource allocation and program sustainability. This paper aims to identify predictors and measure the performance of models used to predict the risk of IIT among People Living with HIV (PLHIV) on antiretroviral therapy (ART). We trained multiple supervised ML algorithms on de-identified client-level electronic medical records data from a cohort of PLHIV across four Nigerian states. Merged demographic, clinic, pharmacy, and laboratory data were included as potential predictor variables in multiple models. The study analyzed data from 41,394 PLHIV, with 266,520 observations receiving treatment across four Nigerian states. The overall IIT rate was 33.7%, ranging from 17.7% in Cross River State to 42.4% in Niger State. The AdaBoost model demonstrated the best performance, with a sensitivity of 69.2%, specificity of 82.3%, F1 score of 0.678, and PR-AUC and ROC-AUC values of 0.563 and 0.843, respectively. Key predictors included PLHIV prior behavior, visit history, and geographic factors, while demographic features played a lesser role. This study highlights the utility of ML, particularly the AdaBoost model, in stratifying PLHIV by the risk of IIT. By leveraging ML, HIV programs can implement data-driven, targeted interventions to improve care continuity. However, further research is needed to address data biases and contextual challenges in resource-constrained settings.

尼日利亚是全球艾滋病疫情规模第二大的国家，在2030年前实现艾滋病疫情控制目标的进程面临诸多阻碍，其中治疗中断（Interruption in Treatment, IIT）是一项严峻挑战。机器学习（Machine Learning, ML）模型可助力艾滋病防控项目实施精准干预措施，以提升诊疗质量、制定高效早期干预方案，并为优化资源配置与项目可持续性提供决策依据。本研究旨在识别接受抗反转录病毒治疗（Antiretroviral Therapy, ART）的艾滋病病毒感染者（People Living with HIV, PLHIV）的治疗中断风险预测因子，并评估相关模型的预测性能。我们基于尼日利亚四个州的艾滋病病毒感染者队列的去标识化患者级电子病历数据，训练了多种有监督机器学习算法。合并后的人口统计学、临床、药房及实验室检测数据被纳入多款模型，作为潜在预测变量。本研究共分析了来自尼日利亚四个州的41394名艾滋病病毒感染者的数据，相关治疗记录共计266520条。整体治疗中断率为33.7%，各州数据区间为17.7%（克罗斯河州）至42.4%（尼日尔州）。AdaBoost模型展现出最优的预测性能，其灵敏度为69.2%、特异度为82.3%、F1分数为0.678，精确召回曲线下面积（Precision-Recall Area Under the Curve, PR-AUC）与受试者工作特征曲线下面积（Receiver Operating Characteristic Area Under the Curve, ROC-AUC）值分别为0.563与0.843。核心预测因子包括艾滋病病毒感染者的既往行为、就诊史及地理因素，而人口统计学特征的影响相对较小。本研究证实了机器学习，尤其是AdaBoost模型，在基于治疗中断风险对艾滋病病毒感染者进行分层管理中的应用价值。借助机器学习技术，艾滋病防控项目可实施数据驱动的精准干预措施，以提升诊疗连续性。不过，仍需开展进一步研究以解决资源受限环境下的数据偏倚与场景化挑战。