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

Journal: Drug and alcohol dependence
Published Date: Jan 1, 2020

Abstract

BACKGROUND: This longitudinal study explored the utility of machine learning (ML) methodology in predicting the trajectory of severity of substance use from childhood to thirty years of age using a set of psychological and health characteristics.

Authors

  • Ziheng Hu
    School of Pharmacy, University of Pittsburgh, 3501 Terrace Street, Pittsburgh, PA, 15261, USA.
  • Yankang Jing
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, 335 Sutherland Drive, 206 Salk Pavilion, Pittsburgh, Pennsylvania, 15261, USA.
  • Ying Xue
    Beijing Centers for Preventive Medical Research, Beijing 100013, China.
  • Peihao Fan
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
  • Lirong Wang
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15261, USA; NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, Pittsburgh, PA 15261, USA; Drug Discovery Institute, University of Pittsburgh, Pittsburgh, PA 15261, USA. Electronic address: liw30@pitt.edu.
  • Michael Vanyukov
    Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA, 15213, USA. Electronic address: mmv@pitt.edu.
  • Levent Kirisci
    Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA, 15213, USA.
  • Junmei Wang
    Department of Pharmaceutical Sciences, Computational Chemical Genomics Screen Center, School of Pharmacy, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA, 15213, USA; Department of Pharmaceutical Sciences, School of Pharmacy, NIDA National Center of Excellence for Computational Drug Abuse Research, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA, 15213, USA. Electronic address: junmei.wang@pitt.edu.
  • Ralph E Tarter
    Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA, 15213, USA.
  • Xiang-Qun Xie

Keywords

External Resources

Popular Topics
Recent Journals