Prediction of adverse events risk in patients with comorbid post-traumatic stress disorder and alcohol use disorder using electronic medical records by deep learning models.

Abstract

Authors

• Oshin Miranda
  Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, 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.
• Xiguang Qi
  Computational Chemical Genomics Screening Center, Department of Pharmaceutical Sciences/School of Pharmacy, University of Pittsburgh, Pittsburgh, PA 15213, USA.
• Haohan Wang
  Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA†Equal Contribution, haohanw@cs.cmu.edu.
• M Daniel Brannock
  RTI International, Durham, NC 27709, USA.
• Thomas Kosten
  Menninger Department of Psychiatry, Baylor College of Medicine, Houston, TX 77030, USA.
• Neal David Ryan
  Department of Psychiatry, School of Medicine, University of Pittsburgh, Pittsburgh, PA 15213, USA.
• Levent Kirisci
  Department of Pharmaceutical Sciences, School of Pharmacy, University of Pittsburgh, 3501 Terrace St, Pittsburgh, PA, 15213, USA.
• 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.

Keywords

External Resources