Using machine learning approaches to predict timely clinic attendance and the uptake of HIV/STI testing post clinic reminder messages

• Published in: Scientific reports 2022-05, Vol.12 (1), p.8757-8757, Article 8757
• Main Authors: Xu, Xianglong, Fairley, Christopher K., Chow, Eric P. F., Lee, David, Aung, Ei T., Zhang, Lei, Ong, Jason J.
• Format: Article
• Language: English
• Subjects: 692/699/255; 692/700/228; 692/700/478; Bayes Theorem; Bayesian analysis; Health care; HIV; HIV Infections - diagnosis; HIV Infections - epidemiology; Homosexuality, Male; Human immunodeficiency virus; Humanities and Social Sciences; Humans; Learning algorithms; Machine Learning; Male; Mass Screening; multidisciplinary; Prediction models; Regression analysis; Science; Science (multidisciplinary); Sexual and Gender Minorities; Sexual Behavior; Sexual health; Sexually transmitted diseases; Sexually Transmitted Diseases - diagnosis; STD
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-022-12033-7

Timely and regular testing for HIV and sexually transmitted infections (STI) is important for controlling HIV and STI (HIV/STI) among men who have sex with men (MSM). We established multiple machine learning models (e.g., logistic regression, lasso regression, ridge regression, elastic net regression, support vector machine, k-nearest neighbour, naïve bayes, random forest, gradient boosting machine, XGBoost, and multi-layer perceptron) to predict timely (i.e., within 30 days) clinic attendance and HIV/STI testing uptake after receiving a reminder message via short message service (SMS) or email). Our study used 3044 clinic consultations among MSM within 12 months after receiving an email or SMS reminder at the Melbourne Sexual Health Centre between April 11, 2019, and April 30, 2020. About 29.5% [899/3044] were timely clinic attendance post reminder messages, and 84.6% [761/899] had HIV/STI testing. The XGBoost model performed best in predicting timely clinic attendance [mean [SD] AUC 62.8% (3.2%); F1 score 70.8% (1.2%)]. The elastic net regression model performed best in predicting HIV/STI testing within 30 days [AUC 82.7% (6.3%); F1 score 85.3% (1.8%)]. The machine learning approach is helpful in predicting timely clinic attendance and HIV/STI re-testing. Our predictive models could be incorporated into clinic websites to inform sexual health care or follow-up service.