Analysis of substance use and its outcomes by machine learning I. Childhood evaluation of liability to substance use disorder.

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

Abstract

BACKGROUND: Substance use disorder (SUD) exacts enormous societal costs in the United States, and it is important to detect high-risk youths for prevention. Machine learning (ML) is the method to find patterns and make prediction from data. We hypothesized that ML identifies the health, psychological, psychiatric, and contextual features to predict SUD, and the identified features predict high-risk individuals to develop SUD.

Authors

  • 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.
  • Ziheng Hu
    School of Pharmacy, University of Pittsburgh, 3501 Terrace Street, Pittsburgh, PA, 15261, USA.
  • Peihao Fan
    Department of Pharmaceutical Sciences and Computational Chemical Genomics Screening Center, School of Pharmacy, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America.
  • Ying Xue
    Beijing Centers for Preventive Medical Research, Beijing 100013, China.
  • 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.
  • Ralph E Tarter
    Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA, 15213, USA.
  • 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.
  • Michael Vanyukov
    Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA, 15213, USA. Electronic address: mmv@pitt.edu.
  • Xiang-Qun Xie

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