Abstract:
. Illicit substance use (ISU) is a major public health problem and a
significant cause of morbidity and mortality globally. Early assessment of risk
behaviour, predicting, identifying risk factors, and detecting illicit substance use
become imperative to reduce the burden. Unfortunately, current digital tools for
early detection and modelling ISU are largely ineffective and sometimes inacces
sible. Data-driven artificial intelligence (AI) models can assist in alleviating the
burdenandtacklingillicit substance use buttheir adoption anduseremainnascent.
ThisstudyappliedthePRISMAmodeltoconductasystematicliteraturereviewon
the application of artificial intelligence models to tackle illicit substance use. The
study revealed that elastic net, artificial neural networks support vector machines,
random forest, logistic regression, KNN, decision trees and deep learning mod
els have been used to predict illicit substance use. These models were applied
to tackle different substance classes, including alcohol, cannabis, hallucinogens,
tobacco, opioids, sedatives, and hypnotics among others. The models were trained
and tested using various substance use data from social media platforms and risk
factors such as socioeconomic and demographic data, behavioural, phenotypic
characteristics, and psychopathology data. Understanding the impact of these
risk factors can assist policymakers and health workers in effective screening,
assessing risk behaviours and, most importantly, predicting illicit substance use.
UsingAImodelsandriskfactorstodevelopdata-drivenintelligentapplicationsfor
monitoring, modelling, and predicting illicit substance use can expedite the early
implementation of interventions to reduce the associated adverse consequences.
