Analysis of substance use and its outcomes by machine learning: II. Derivation and prediction of the trajectory of substance use severity.

Hu, Ziheng; Jing, Yankang; Xue, Ying; Fan, Peihao; Wang, Lirong; Vanyukov, Michael; Kirisci, Levent; Wang, Junmei; Tarter, Ralph E; Xie, Xiang-Qun

Hu, Ziheng; Jing, Yankang; Xue, Ying; Fan, Peihao; Wang, Lirong; Vanyukov, Michael; Kirisci, Levent; Wang, Junmei; Tarter, Ralph E; Xie, Xiang-Qun.

Afiliação

Hu Z; Department of Pharmaceutical Sciences, Computational Chemical Genomics Screen Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA; NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA, USA.
Jing Y; Department of Pharmaceutical Sciences, Computational Chemical Genomics Screen Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA; NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA, USA.
Xue Y; Department of Pharmaceutical Sciences, Computational Chemical Genomics Screen Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA; NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA, USA.
Fan P; Department of Pharmaceutical Sciences, Computational Chemical Genomics Screen Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA; NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA, USA.
Wang L; Department of Pharmaceutical Sciences, Computational Chemical Genomics Screen Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA; NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA, USA.
Vanyukov M; Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA.
Kirisci L; Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA.
Wang J; NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: junmei.wang@pitt.edu.
Tarter RE; Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: tarter@pitt.edu.
Xie XQ; Department of Pharmaceutical Sciences, Computational Chemical Genomics Screen Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA, USA; NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA, USA. Electronic address: xix15@

Drug Alcohol Depend ; 206: 107604, 2020 01 01.

Article em En | MEDLINE | ID: mdl-31615693

Assuntos

Aprendizado de Máquina; Técnicas Psicológicas; Índice de Gravidade de Doença; Transtornos Relacionados ao Uso de Substâncias/diagnóstico; Adolescente; Adulto; Criança; Feminino; Humanos; Estudos Longitudinais; Masculino; Valor Preditivo dos Testes; Reprodutibilidade dos Testes; Adulto Jovem

Palavras-chave

Machine learning; Random Forest; Substance misuse prevention; Substance use disorder; Trajectory analysis

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Índice de Gravidade de Doença / Técnicas Psicológicas / Transtornos Relacionados ao Uso de Substâncias / Aprendizado de Máquina Tipo de estudo: Evaluation_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Aspecto: Patient_preference Limite: Adolescent / Adult / Child / Female / Humans / Male Idioma: En Revista: Drug Alcohol Depend Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Estados Unidos País de publicação: Irlanda

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