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dc.contributor.authorWang, Bo
dc.contributor.authorLiu, Feifan
dc.contributor.authorDeveaux, Lynette
dc.contributor.authorAsh, Arlene S.
dc.contributor.authorGosh, Samiran
dc.contributor.authorLi, Xiaoming
dc.contributor.authorRundensteiner, Elke
dc.contributor.authorCottrell, Lesley
dc.contributor.authorAdderley, Richard
dc.contributor.authorStanton, Bonita
dc.date2022-08-11T08:10:36.000
dc.date.accessioned2022-08-23T17:14:18Z
dc.date.available2022-08-23T17:14:18Z
dc.date.issued2021-05-01
dc.date.submitted2021-09-23
dc.identifier.citation<p>Wang B, Liu F, Deveaux L, Ash A, Gosh S, Li X, Rundensteiner E, Cottrell L, Adderley R, Stanton B. Adolescent HIV-related behavioural prediction using machine learning: a foundation for precision HIV prevention. AIDS. 2021 May 1;35(Suppl 1):S75-S84. doi: 10.1097/QAD.0000000000002867. PMID: 33867490; PMCID: PMC8133351. <a href="https://doi.org/10.1097/QAD.0000000000002867">Link to article on publisher's site</a></p>
dc.identifier.issn0269-9370 (Linking)
dc.identifier.doi10.1097/QAD.0000000000002867
dc.identifier.pmid33867490
dc.identifier.urihttp://hdl.handle.net/20.500.14038/46927
dc.description.abstractBACKGROUND: Precision prevention is increasingly important in HIV prevention research to move beyond universal interventions to those tailored for high-risk individuals. The current study was designed to develop machine learning algorithms for predicting adolescent HIV risk behaviours. METHODS: Comprehensive longitudinal data on adolescent risk behaviours, perceptions, peer and family influence, and neighbourhood risk factors were collected from 2564 grade-10 students at baseline followed for 24 months over 2008-2012. Machine learning techniques [support vector machine (SVM) and random forests] were applied to innovatively leverage longitudinal data for robust HIV risk behaviour prediction. In this study, we focused on two adolescent risk behaviours: had ever had sex and had multiple sex partners. Twenty percent of the data were withheld for model testing. RESULTS: The SVM model with cost-sensitive learning achieved the highest sensitivity, at 79.1%, specificity of 75.4% with AUC of 0.86 in predicting multiple sex partners on the training data (10-fold cross-validation), and sensitivity of 79.7%, specificity of 76.5% with AUC of 0.86 on the testing data. The random forest model obtained the best performance in predicting had ever had sex, yielding the sensitivity of 78.5%, specificity of 73.1% with AUC of 0.84 on the training data and sensitivity of 82.7%, specificity of 75.3% with AUC of 0.87 on the testing data. CONCLUSION: Machine learning methods can be used to build effective prediction model(s) to identify adolescents who are likely to engage in HIV risk behaviours. This study builds a foundation for targeted intervention strategies and informs precision prevention efforts in school-setting.
dc.language.isoen_US
dc.relation<p><a href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=pubmed&cmd=Retrieve&list_uids=33867490&dopt=Abstract">Link to Article in PubMed</a></p>
dc.relation.urlhttps://doi.org/10.1097/qad.0000000000002867
dc.subjectadolescent HIV risk behaviour
dc.subjectmachine learning
dc.subjectmultiple sex partners
dc.subjectprediction
dc.subjectArtificial Intelligence and Robotics
dc.subjectBehavior and Behavior Mechanisms
dc.subjectEpidemiology
dc.subjectVirus Diseases
dc.titleAdolescent HIV-related behavioural prediction using machine learning: a foundation for precision HIV prevention
dc.typeJournal Article
dc.source.journaltitleAIDS (London, England)
dc.source.volume35
dc.source.issueSuppl 1
dc.identifier.legacycoverpagehttps://escholarship.umassmed.edu/qhs_pp/1406
dc.identifier.contextkey25084002
html.description.abstract<p>BACKGROUND: Precision prevention is increasingly important in HIV prevention research to move beyond universal interventions to those tailored for high-risk individuals. The current study was designed to develop machine learning algorithms for predicting adolescent HIV risk behaviours.</p> <p>METHODS: Comprehensive longitudinal data on adolescent risk behaviours, perceptions, peer and family influence, and neighbourhood risk factors were collected from 2564 grade-10 students at baseline followed for 24 months over 2008-2012. Machine learning techniques [support vector machine (SVM) and random forests] were applied to innovatively leverage longitudinal data for robust HIV risk behaviour prediction. In this study, we focused on two adolescent risk behaviours: had ever had sex and had multiple sex partners. Twenty percent of the data were withheld for model testing.</p> <p>RESULTS: The SVM model with cost-sensitive learning achieved the highest sensitivity, at 79.1%, specificity of 75.4% with AUC of 0.86 in predicting multiple sex partners on the training data (10-fold cross-validation), and sensitivity of 79.7%, specificity of 76.5% with AUC of 0.86 on the testing data. The random forest model obtained the best performance in predicting had ever had sex, yielding the sensitivity of 78.5%, specificity of 73.1% with AUC of 0.84 on the training data and sensitivity of 82.7%, specificity of 75.3% with AUC of 0.87 on the testing data.</p> <p>CONCLUSION: Machine learning methods can be used to build effective prediction model(s) to identify adolescents who are likely to engage in HIV risk behaviours. This study builds a foundation for targeted intervention strategies and informs precision prevention efforts in school-setting.</p>
dc.identifier.submissionpathqhs_pp/1406
dc.contributor.departmentPopulation and Quantitative Health Sciences
dc.contributor.departmentUMass Chan Analytics
dc.contributor.departmentBiostatistics and Health Services Research
dc.source.pagesS75-S84


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