Journal Article

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

Günthard, Huldrych F., Marzel, Alex, Schmid, Patrick, Braun, Dominique L., Kouyos, Roger D., Schmidt, Axel J., Doco Lecompte, Thanh, Salazar-Vizcaya, Luisa, Rauch, Andri, Mugglin, Catrina, Roth, Jan A., Andresen, Sara, Bernasconi, Enos, Balakrishna, Suraj, Darling, Katharine EA

2022

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Analysis / Bayesian analysis / Biology and Life Sciences / Cluster analysis / Clustering / Cohort analysis / Computer and Information Sciences / Criteria / Disease transmission / Epidemic models / Epidemiology / Ethics / Evaluation / Gay men / Grants / HIV / HIV patients / Human immunodeficiency virus / Infections / Laboratories / Learning algorithms / Likelihood ratio / Machine learning / Mathematical models / Medical research / Medicine and Health Sciences / People and Places / Performance prediction / Research and Analysis Methods / Risk analysis / Risk factors / Sex / Sexual behavior / Sexually transmitted diseases / Social Sciences / Statistical analysis / Statistical methods / STD / Subgroups / Syphilis / Time series / Unsupervised learning