A machine learning approach to predict volatile substance abuse for drug risk analysis

Nath, Priyanka; Kilam, Sumran; Swetapadma, Aleena

doi:10.1109/icrcicn.2017.8234516

Cited by 11 publications

(3 citation statements)

References 7 publications

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“…The relation between personality traits and illicit substance use has been well-researched for many decades. Sieber (1981) as well as Impulsivity and Sensation Seeking (Kumari et al, 2018;Liu, 2019;Nath et al, 2017;Qiao et al, 2019). In these studies, different kinds of ML algorithms (e.g., random forests or artificial neural networks) were used to predict the usage of various licit or illicit drugs.…”

Section: Tell Me Who You Are and I Tell You What You Smokementioning

confidence: 99%

Everything has its Price: Foundations of Cost-Sensitive Learning and its Application in Psychology

Sterner¹,

Goretzko²,

Pargent³

2021

Preprint

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Psychology has seen an increase in machine learning (ML) methods. In many applications, observations are classified into one of two groups (binary classification). Off-the-shelf classification algorithms assume that the costs of a misclassification (false-positive or false-negative) are equal. Because this is often not reasonable (e.g., in clinical psychology), cost-sensitive learning (CSL) methods can take different cost ratios into account. We present the mathematical foundations and introduce a taxonomy of the most commonly used CSL methods, before demonstrating their application and usefulness on psychological data, i.e., the drug consumption dataset ($N = 1885$) from the UCI Machine Learning Repository. In our example, all demonstrated CSL methods noticeably reduce mean misclassification costs compared to regular ML algorithms. We discuss the necessity for researchers to perform small benchmarks of CSL methods for their own practical application. Thus, our open materials provide R code, demonstrating how CSL methods can be applied within the mlr3 framework (https://osf.io/cvks7/).

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Section: Tell Me Who You Are and I Tell You What You Smokementioning

confidence: 99%

Everything has its Price: Foundations of Cost-Sensitive Learning and its Application in Psychology

Sterner¹,

Goretzko²,

Pargent³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, other machine learning models, which are capable of binary classification through optimization of weights (e.g., finding β * i equivalents) for given attributes, can be used as well. Experimenting with different machine learning models, which can handle non-linear patterns, may also allow us to obtain better sensitivity and specificity [24,25,[31][32][33].…”

Section: Discussionmentioning

confidence: 99%

Towards the Development of a Substance Abuse Index (SEI) through Informatics

2021

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Substance abuse or drug dependence is a prevalent phenomenon, and is on the rise in United States. Important contributing factors for the prevalence are the addictive nature of certain medicinal/prescriptive drugs, individual dispositions (biological, physiological, and psychological), and other external influences (e.g., pharmaceutical advertising campaigns). However, currently there is no comprehensive computational or machine learning framework that allows systematic studies of substance abuse and its factors with majority of the works using subjective surveys questionnaires and focusing on classification techniques. Lacking standardized methods and/or measures to prescribe medication and to study substance abuse makes it difficult to advance through collective research efforts. Thus, in this paper, we propose to test the feasibility of developing a, objective substance effect index, SEI, that can measure the tendency of an individual towards substance abuse. To that end, we combine the benefits of Electronics Medical Records (EMR) with machine learning technology by defining SEI as a function of EMR data and using logistics regression to obtain a closed form expression for SEI. We conduct various evaluations to validate the proposed model, and the results show that further work towards the development of SEI will not only provide researchers with standard computational measure for substance abuse, but may also allow them to study certain attribute interactions to gain further insights into substance abuse tendencies.

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“…Thanks to Fehrmann et al (2017) making their data set on drug consumption and personality publicly available on the UCI ML Repository (Dua & Graff, 2019), another body of research has emerged over the last few years: Many studies have been conducted with the goal of predicting drug consumption on the basis of the Big Five as well as impulsivity and sensation seeking (Kumari et al, 2018; Liu, 2019; Nath et al, 2017; Qiao et al, 2019). In these studies, different kinds of ML algorithms (e.g., random forests or artificial neural networks) were used to predict the usage of various licit or illicit drugs.…”

Section: In Psychological Sciencementioning

confidence: 99%

Everything has its price: Foundations of cost-sensitive machine learning and its application in psychology.

Sterner

Goretzko

Pargent

2023

Psychological Methods

View full text Add to dashboard Cite

Psychology has seen an increase in the use of machine learning (ML) methods. In many applications, observations are classified into one of two groups (binary classification). Off-the-shelf classification algorithms assume that the costs of a misclassification (false positive or false negative) are equal. Because this is often not reasonable (e.g., in clinical psychology), cost-sensitive machine learning (CSL) methods can take different cost ratios into account. We present the mathematical foundations and introduce a taxonomy of the most commonly used CSL methods, before demonstrating their application and usefulness on psychological data, that is, the drug consumption data set (N = 1, 885) from the University of California Irvine ML Repository. In our example, all demonstrated CSL methods noticeably reduced mean misclassification costs compared to regular ML algorithms. We discuss the necessity for researchers to perform small benchmarks of CSL methods for their own practical application. Thus, our open materials provide R code, demonstrating how CSL methods can be applied within the mlr3 framework (https://osf.io/cvks7/).

show abstract

A machine learning approach to predict volatile substance abuse for drug risk analysis

Cited by 11 publications

References 7 publications

Everything has its Price: Foundations of Cost-Sensitive Learning and its Application in Psychology

Everything has its Price: Foundations of Cost-Sensitive Learning and its Application in Psychology

Towards the Development of a Substance Abuse Index (SEI) through Informatics

Everything has its price: Foundations of cost-sensitive machine learning and its application in psychology.

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