[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122635-en":3,"doc-seo-122635-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122635,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder - read online free","Substance use disorder (SUD) is a frequent comorbidity in bipolar disorder (BD), intensifying disease severity and underscoring the need for early identification of risk factors. This study used machine-learning models to detect factors linked to distinct SUD types in people with BD. Data from 508 BD participants were analyzed, with lifetime SUD defined via DSM criteria and random forest features carried into multiple logistic regressions.","Journal of  \nClinical Medicine  \nArticle  \nMachine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder  \nVincenzo Oliva 1,2,†, Michele De Prisco 1,3,†, Maria Teresa Pons-Cabrera 4, Pablo Guzm¡n 4, Gerard Anmella 1, Diego Hidalgo-Mazzei 1, Iria Grande 1, Giuseppe Fanelli 2,5, Chiara Fabbri 2,6, Alessandro Serretti 2, Michele Fornaro 3, Felice Iasevoli 3, Andrea de Bartolomeis 3, Andrea Murru 1, Eduard Vieta 1, * and Giovanna Fico 1  \nCitation: Oliva, V.; De Prisco, M.; Pons-Cabrera, M.T.; Guzmán, P.; Anmella, G.; Hidalgo-Mazzei, D.; Grande, I.; Fanelli, G.; Fabbri, C.; Serretti, A.; et al. Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder. J. Clin. Med. 2022, 11, 3935. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)jcm11143935  \nAcademic Editors: Ana Adan and Marta Torrens  \nReceived: 9 June 2022  \nAccepted: 4 July 2022  \nPublished: 6 July 2022  \nPublisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.  \nCopyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Bipolar and Depressive Disorders Unit, Institute of Neurosciences, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, 170 Villarroel St., 12-0, 08036 Barcelona, Catalonia, Spain; [voliva@clinic.cat](voliva@clinic.cat) (V.O.);  \n[mdeprisco@clinic.cat](mdeprisco@clinic.cat) (M.D.P.); [anmella@clinic.cat](anmella@clinic.cat) (G.A.); [dahidalg@clinic.cat](dahidalg@clinic.cat) (D.H.-M.);  \n[igrande@clinic.cat](igrande@clinic.cat) (I.G.); [amurru@clinic.cat](amurru@clinic.cat) (A.M.); gfi[co@clinic.cat](co@clinic.cat) (G.F.)  \n2 Department of Biomedical and Neuromotor Sciences, University of Bologna, 40123 Bologna, Italy; [giuseppe.fanelli5@unibo.it](giuseppe.fanelli5@unibo.it) (G.F.); [chiara.fabbri@yahoo.it](chiara.fabbri@yahoo.it) (C.F.); [alessandro.serretti@unibo.it](alessandro.serretti@unibo.it) (A.S.)  \n3 Section of Psychiatry, Department of Neuroscience, Reproductive Science and Odontostomatology, Federico II University of Naples, 80131 Naples, Italy; [dott.fornaro@gmail.com](dott.fornaro@gmail.com) (M.F.); [felice.iasevoli@unina.it](felice.iasevoli@unina.it) (F.I.); [adebarto@unina.it](adebarto@unina.it) (A.d.B.)  \n4 Addictions Unit, Department of Psychiatry and Psychology, Institute of Neuroscience, Hospital Clinic, University of Barcelona, IDIBAPS, CIBERSAM, 170 Villarroel St., 12-0, 08036 Barcelona, Catalonia, Spain; [mtpons@clinic.cat](mtpons@clinic.cat) (M.T.P.-C.); [prguzman@clinic.cat](prguzman@clinic.cat) (P.G.)  \n5 Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, 6525 GD Nijmegen, The Netherlands  \n6 Social, Genetic & Developmental Psychiatry Centre, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 9NU, UK  \n* Correspondence: [evieta@clinic.cat](evieta@clinic.cat)[ ](evieta@clinic.cat)† These authors contributed equally to this work.  \nAbstract: Substance use disorder (SUD) is a common comorbidity in individuals with bipolar disorder (BD), and it is associated with a severe course of illness, making early identification of the risk factors for SUD in BD warranted. We aimed to identify, through machine-learning models, the factors associated with different types of SUD in BD. We recruited 508 individuals with BD from a specialized unit. Lifetime SUDs were defined according to the DSM criteria. Random forest (RF) models were trained to identify the presence of (i) any (SUD) in the total sample,(ii) alcohol use disorder (AUD) in the total sample,(iii) AUD co-occurrence","cbCaigEHbp3cku28","https://ap.wps.com/l/cbCaigEHbp3cku28","pdf",813813,1,13,"English","en",105,"# Introduction\n# Methods\n## Participants and Measures\n## Machine-Learning and Statistical Analyses\n# Results\n## Prediction Performance\n## Associated Clinical Factors\n# Discussion\n# Conclusions","[{\"question\":\"What was the goal of the machine-learning analysis in people with bipolar disorder?\",\"answer\":\"To identify factors associated with different types of substance use disorders (SUD) in people with bipolar disorder using machine-learning models.\"},{\"question\":\"How were substance use disorders defined in the study?\",\"answer\":\"Lifetime SUDs were defined according to DSM criteria.\"},{\"question\":\"Which model approach was used to make SUD predictions, and how were selected variables handled afterward?\",\"answer\":\"Random forest (RF) models predicted SUD presence, and relevant variables selected by RF were then used as independent variables in multiple logistic regressions.\"}]","Machine Learning Prediction of Comorbid Substance Use Disorders among People with Bipolar Disorder - 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