Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men.

Machine learning is increasingly introduced into medical fields, yet there is limited evidence for its benefit over more commonly used statistical methods in epidemiological studies. We introduce an unsupervised machine learning framework for longitudinal features and evaluate it using sexual behaviour data from the last 20 years from over 3'700 participants in the Swiss HIV Cohort Study (SHCS). We use hierarchical clustering to find subgroups of men who have sex with men in the SHCS with similar sexual behaviour up to May 2017, and apply regression to test whether these clusters enhance predictions of sexual behaviour or sexually transmitted diseases (STIs) after May 2017 beyond what can be predicted with conventional parameters. We find that behavioural clusters enhance model performance according to likelihood ratio test, Akaike information criterion and area under the receiver operator characteristic curve for all outcomes studied, and according to Bayesian information criterion for five out of ten outcomes, with particularly good performance for predicting future sexual behaviour and recurrent STIs. We thus assess a methodology that can be used as an alternative means for creating exposure categories from longitudinal data in epidemiological models, and can contribute to the understanding of time-varying risk factors.

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Copyright: © 2022 Andresen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Déclaration de conflit d'intérêts

I have read the journal’s policy and the authors of this manuscript have the following competing interests: DLB reports honoraria and travel grants outside of the submitted work from Gilead, ViiV and Merck. HFG, outside of this study, reports grants from Swiss HIV Cohort Study, grants from Swiss National Science Foundation, during the conduct of the study; grants from Swiss HIV Cohort Study, grants from Swiss National Science Foundation, grants from NIH, grants from Gilead unrestricted research grant, personal fees from Advisor/consultant for Merck, ViiV healthcare and Gilead sciences and member of DSMB for Merck, grants from Yvonne Jacob Foundation. KEAD’s institution has received research funding unrelated to this publication from Gilead and sponsorship to specialist meetings from MSD. AR reports fees for sitting on advisory boards from Merck Sharp & Dohme and Gilead Sciences; travel grants from Gilead Sciences, Pfizer, and AbbVie; and a research grant from Gilead Sciences, outside of the submitted work. All fees were paid to AR’s institution and not to AR personally.