Lin Bing, Feng Shihan, Liu Jiaxiu, Li Kangjie, Shi Guiqian, Zhong Xiaoni
School of Public Health, Chongqing Medical University, Chongqing, PR China.
Research Center for Medicine and Social Development, Chongqing, PR China.
Int J Clin Health Psychol. 2024 Jul-Sep;24(3):100490. doi: 10.1016/j.ijchp.2024.100490. Epub 2024 Aug 8.
Men who have sex with men (MSM) are at a high risk for HIV infection. While pre-exposure prophylaxis (PrEP) is an effective oral preventive strategy, its success is largely dependent on consistent medication adherence.
The aim of this study was to develop the machine learning web application and evaluate the performance in predicting PrEP adherence.
The PrEP prospective cohort study of the MSM population conducted in Western China from 2019 to 2023, and we collected adherence data and personal characteristics data from 747 MSM. Predictor variables were screened and the performance of several machine learning methods in predicting nonadherent behaviors were compared.
A total of 11 candidate variables were screened that predicted nonadherent behaviors. We developed and evaluated five machine learning models that performed well in predicting adherence. Attitudes of male sexual partners, self-efficacy, HIV testing, number of male sexual partners, and risk perception were the most important predictors of adherence. The optimal prediction model was displayed in a shiny web application for online calculation of the probability of occurrence of nonadherent behaviors among MSM.
Machine learning performed well in predicting nonadherent behaviors among MSM. An interactive and intuitive web application can help identify individuals who may have nonadherent behaviors, resulting in improved medication adherence and increased prevention efficacy.
男男性行为者（MSM）感染艾滋病毒的风险很高。虽然暴露前预防（PrEP）是一种有效的口服预防策略，但其成功很大程度上取决于持续的药物依从性。
本研究的目的是开发机器学习网络应用程序并评估其在预测PrEP依从性方面的性能。
2019年至2023年在中国西部对男男性行为人群进行PrEP前瞻性队列研究，我们收集了747名男男性行为者的依从性数据和个人特征数据。筛选预测变量并比较几种机器学习方法在预测不依从行为方面的性能。
共筛选出11个预测不依从行为的候选变量。我们开发并评估了五个在预测依从性方面表现良好的机器学习模型。男性性伴侣的态度、自我效能感、艾滋病毒检测、男性性伴侣数量和风险认知是依从性的最重要预测因素。最佳预测模型显示在一个闪亮的网络应用程序中，用于在线计算男男性行为者中不依从行为发生的概率。
机器学习在预测男男性行为者的不依从行为方面表现良好。一个交互式且直观的网络应用程序可以帮助识别可能有不依从行为的个体，从而提高药物依从性并增强预防效果。
