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dc.contributorShaban-Nejad, Arash
dc.contributorMichalowski, Martin
dc.contributorBianco, Simone
dc.contributor.authorSylvestre, Marie-Pierre
dc.contributor.authorDe Montigny, Simon
dc.contributor.authorBoulanger, Laurence
dc.contributor.authorGoulet, Danick
dc.contributor.authorDoré, Isabelle
dc.contributor.authorO'Loughlin, Jennifer
dc.contributor.authorHaddad, Slim
dc.contributor.authorBélanger, Richard E.
dc.contributor.authorLeatherdale, Scott
dc.date.accessioned2022-08-01T11:54:26Z
dc.date.availableNO_RESTRICTIONfr
dc.date.available2022-08-01T11:54:26Z
dc.date.issued2022-03-09
dc.identifier.urihttp://hdl.handle.net/1866/26732
dc.publisherSpringerfr
dc.subjectCannabis usefr
dc.subjectAdolescentsfr
dc.subjectPrognostic toolfr
dc.titleA prognostic tool to identify youth at risk of at least weekly cannabis usefr
dc.typeChapitre de livre / Book chapterfr
dc.contributor.affiliationUniversité de Montréal. École de santé publique. Département de médecine sociale et préventivefr
dc.identifier.doi10.1007/978-3-030-93080-6_4
dcterms.abstractWe developed and validated an 8-item prognostic tool to identify youth at risk of initiating frequent (i.e., at least weekly) cannabis use in the next year. The tool, which aims to identify youth who would benefit most from clinician intervention, can be completed by the patient or clinician using a computer or smart phone application prior to or during a clinic visit. Methodological challenges in developing the tool included selecting a parsimonious model from a set of correlated predictors with missing data. We implemented Bach’s bolasso algorithm which combines lasso with bootstrap and investigated the performance of the prognostic tool in new data collected in a different time period (temporal validation) and in another location (geographic validation). The tool showed adequate discrimination abilities, as reflected by a c-statistic above 0.8, in both validation samples. Most predictors selected into the tool pertained to substance use including use of cigarettes, e-cigarettes, alcohol and energy drinks mixed with alcohol, but not to mental or physical health.fr
dcterms.isPartOfurn:ISBN:9783030930790fr
dcterms.languageengfr
UdeM.ReferenceFournieParDeposantSylvestre, MP., de Montigny, S., Boulanger, L., Goulet, D., Doré, I., O’Loughlin, J.,Haddad, S., Bélanger, R.E. and Leatherdale, S. (2022). A Prognostic Tool to Identify Youth at Risk of at Least Weekly Cannabis Use. In: Shaban-Nejad, A., Michalowski, M., Bianco, S. (eds) AI for Disease Surveillance and Pandemic Intelligence. W3PHAI 2021. Studies in Computational Intelligence, vol 1013. Springer, Cham. https://doi.org/10.1007/978-3-030-93080-6_4 ISBN : 978-3-030-93079-0 pages: 37 à 48fr
oaire.citationTitleAI for disease surveillance and pandemic intelligence : intelligent disease detection in actionfr
oaire.citationStartPage37fr
oaire.citationEndPage48fr


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