부산대학교 도서관

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. Author summary: Machine learning tools are becoming increasingly important in medical research. Yet it is not entirely clear whether these tools perform better than conventional ones when it comes to research on a population level. Here we designed and tested a machine learning method which we use to predict sexually transmitted diseases among HIV-positive men who have sex with men in Switzerland. We used a machine learning algorithm to find groups of men with similar sexual behaviour in the last twenty years, and found that considering these groups in addition to conventional risk factors yielded more accurate predictions of who would be diagnosed with an STI. With this we hope to shed some light on how and to what extent machine learning can help predict and prevent infectious diseases. [ABSTRACT FROM AUTHOR]