Deep learning neural network derivation and testing to distinguish acute poisonings.

Journal: Expert opinion on drug metabolism & toxicology
PMID: 37395108

Abstract

INTRODUCTION: Acute poisoning is a significant global health burden, and the causative agent is often unclear. The primary aim of this pilot study was to develop a deep learning algorithm that predicts the most probable agent a poisoned patient was exposed to from a pre-specified list of drugs.

Authors

  • Omid Mehrpour
    Data Science Institute, Southern Methodist University, Dallas, TX, USA. omehrpour@smu.edu.
  • Christopher Hoyte
    Student Research Committee, Birjand University of Medical Sciences, Birjand, Iran.
  • Abdullah Al Masud
    Department of Engineering, Hiperdyne Corporation, Tokyo, Japan.
  • Ashis Biswas
    Department of Computer Science and Engineering, University of Colorado, Denver, CO, USA.
  • Jonathan Schimmel
    Department of Emergency Medicine, Division of Medical Toxicology, Mount Sinai Hospital Icahn School of Medicine, New York, NY, USA.
  • Samaneh Nakhaee
    Medical Toxicology and Drug Abuse Research Center (MTDRC), Birjand University of Medical Sciences (BUMS), Birjand, Iran.
  • Mohammad Sadegh Nasr
    Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, USA.
  • Heather Delva-Clark
    CPC Clinical Research, Aurora, Colorado, USA.
  • Foster Goss
    University of Colorado Hospital, Aurora, CO, USA.

Keywords

External Resources

Popular Topics
Recent Journals