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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 behavi...

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Autores principales: Andresen, Sara, Balakrishna, Suraj, Mugglin, Catrina, Schmidt, Axel J., Braun, Dominique L., Marzel, Alex, Doco Lecompte, Thanh, Darling, Katharine EA, Roth, Jan A., Schmid, Patrick, Bernasconi, Enos, Günthard, Huldrych F., Rauch, Andri, Kouyos, Roger D., Salazar-Vizcaya, Luisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9642906/
https://www.ncbi.nlm.nih.gov/pubmed/36302041
http://dx.doi.org/10.1371/journal.pcbi.1010559
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author Andresen, Sara
Balakrishna, Suraj
Mugglin, Catrina
Schmidt, Axel J.
Braun, Dominique L.
Marzel, Alex
Doco Lecompte, Thanh
Darling, Katharine EA
Roth, Jan A.
Schmid, Patrick
Bernasconi, Enos
Günthard, Huldrych F.
Rauch, Andri
Kouyos, Roger D.
Salazar-Vizcaya, Luisa
author_facet Andresen, Sara
Balakrishna, Suraj
Mugglin, Catrina
Schmidt, Axel J.
Braun, Dominique L.
Marzel, Alex
Doco Lecompte, Thanh
Darling, Katharine EA
Roth, Jan A.
Schmid, Patrick
Bernasconi, Enos
Günthard, Huldrych F.
Rauch, Andri
Kouyos, Roger D.
Salazar-Vizcaya, Luisa
author_sort Andresen, Sara
collection PubMed
description 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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spelling pubmed-96429062022-11-15 Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men Andresen, Sara Balakrishna, Suraj Mugglin, Catrina Schmidt, Axel J. Braun, Dominique L. Marzel, Alex Doco Lecompte, Thanh Darling, Katharine EA Roth, Jan A. Schmid, Patrick Bernasconi, Enos Günthard, Huldrych F. Rauch, Andri Kouyos, Roger D. Salazar-Vizcaya, Luisa PLoS Comput Biol Research Article 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. Public Library of Science 2022-10-27 /pmc/articles/PMC9642906/ /pubmed/36302041 http://dx.doi.org/10.1371/journal.pcbi.1010559 Text en © 2022 Andresen et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Andresen, Sara
Balakrishna, Suraj
Mugglin, Catrina
Schmidt, Axel J.
Braun, Dominique L.
Marzel, Alex
Doco Lecompte, Thanh
Darling, Katharine EA
Roth, Jan A.
Schmid, Patrick
Bernasconi, Enos
Günthard, Huldrych F.
Rauch, Andri
Kouyos, Roger D.
Salazar-Vizcaya, Luisa
Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men
title Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men
title_full Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men
title_fullStr Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men
title_full_unstemmed Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men
title_short Unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among HIV-positive men who have sex with men
title_sort unsupervised machine learning predicts future sexual behaviour and sexually transmitted infections among hiv-positive men who have sex with men
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9642906/
https://www.ncbi.nlm.nih.gov/pubmed/36302041
http://dx.doi.org/10.1371/journal.pcbi.1010559
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