eISSN: 1689-3530
ISSN: 0867-4361
Alcoholism and Drug Addiction/Alkoholizm i Narkomania
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3/2019
vol. 32
 
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abstract:
Letter to the Editor

On the necessity for paradigm shift in psychoactive substances research: the implementation of  machine learning and artificial intelligence

Ahmed Al-Imam
1, 2
,
Marek A. Motyka
3

1.
Department of Anatomy and Cellular Biology, College of Medicine, University of Baghdad, Iraq
2.
CERVO Brain Research Centre, Faculty of Medicine, University of Laval, Quebec, Canada
3.
Institute of Sociology, University of Rzeszow, Poland
Alcohol Drug Addict 2019; 32 (3): 237-242
Online publish date: 2019/12/18
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The twenty-first century is a time of spectacular scientific advances, especially in information technology and the ongoing scope of application of artificial intelligence. The use of the concepts of machine learning is revolutionising medical and paramedical research. These concepts also apply in the study of psychoactive substances, especially to search for information in the global databases of literature, the systematic review of current publications, the prediction of future trends for the spread of these substances all over the world and also to determine patterns of their misuse and all activities linked to their use.

A systematic review of literature conducted in internet databases indicates apparent deficits in research activity as regards the matters concerning machine learning. It is the authors’ opinion that filling this gap will allow a fuller analysis of the phenomenon of psychoactive substance use and identification of emerging tendencies.

The text presents a comprehensive research project applying the proposed concepts, the main aim of which is the evaluation of the prevalence of psychoactive substance use and abuse, including new psychoactive substances at the national and international level, and thus establishing adequate and effective preventive strategies, and developing guidelines for management in crisis situations.
keywords:

Artificial intelligence, Machine learning, Big data, Predictive Analytics, Real-Time Analytics

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