Biomaterial Database

Leading consumption patterns of psychoactive substances in Colombia: A deep neural network-based clustering-oriented embedding approach. 2023

Kevin Palomino, and Carmen R Berdugo, and Jorge I Vélez

Department of Industrial Engineering, Universidad del Norte, Barranquilla, Colombia.

The number of health-related incidents caused using illegal and legal psychoactive substances (PAS) has dramatically increased over two decades worldwide. In Colombia, the use of illicit substances has increased up to 10.3%, while the consumption alcohol and tobacco has increased to 84% and 12%, respectively. It is well-known that identifying drug consumption patterns in the general population is essential in reducing overall drug consumption. However, existing approaches do not incorporate Machine Learning and/or Deep Data Mining methods in combination with spatial techniques. To enhance our understanding of mental health issues related to PAS and assist in the development of national policies, here we present a novel Deep Neural Network-based Clustering-oriented Embedding Algorithm that incorporates an autoencoder and spatial techniques. The primary goal of our model is to identify general and spatial patterns of drug consumption and abuse, while also extracting relevant features from the input data and identifying clusters during the learning process. As a test case, we used the largest publicly available database of legal and illegal PAS consumption comprising 49,600 Colombian households. We estimated and geographically represented the prevalence of consumption and/or abuse of both PAS and non-PAS, while achieving statistically significant goodness-of-fit values. Our results indicate that region, sex, housing type, socioeconomic status, age, and variables related to household finances contribute to explaining the patterns of consumption and/or abuse of PAS. Additionally, we identified three distinct patterns of PAS consumption and/or abuse. At the spatial level, these patterns indicate concentrations of drug consumption in specific regions of the country, which are closely related to specific geographic locations and the prevailing social and environmental contexts. These findings can provide valuable insights to facilitate decision-making and develop national policies targeting specific groups given their cultural, geographic, and social conditions.

UI	MeSH Term	Description	Entries
D002491	Central Nervous System Agents	A class of drugs producing both physiological and psychological effects through a variety of mechanisms. They can be divided into "specific" agents, e.g., affecting an identifiable molecular mechanism unique to target cells bearing receptors for that agent, and "nonspecific" agents, those producing effects on different target cells and acting by diverse molecular mechanisms. Those with nonspecific mechanisms are generally further classed according to whether they produce behavioral depression or stimulation. Those with specific mechanisms are classed by locus of action or specific therapeutic use. (From Gilman AG, et al., Goodman and Gilman's The Pharmacological Basis of Therapeutics, 8th ed, p252)	Central Nervous System Drugs
D003105	Colombia	A country in northern South America, bordering the Caribbean Sea, between Panama and Venezuela, and bordering the north Pacific Ocean, between Ecuador and Panama. The capital is Bogota.
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D000428	Alcohol Drinking	Behaviors associated with the ingesting of ALCOHOLIC BEVERAGES, including social drinking.	Alcohol Consumption,Alcohol Intake,Drinking, Alcohol,Alcohol Drinking Habits,Alcohol Drinking Habit,Alcohol Intakes,Consumption, Alcohol,Drinking Habit, Alcohol,Habit, Alcohol Drinking,Habits, Alcohol Drinking,Intake, Alcohol
D000465	Algorithms	A procedure consisting of a sequence of algebraic formulas and/or logical steps to calculate or determine a given task.	Algorithm
D016000	Cluster Analysis	A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both.	Clustering,Analyses, Cluster,Analysis, Cluster,Cluster Analyses,Clusterings
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron