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Modeling drug detection and diagnosis with the 'drug evaluation and classification program'
(Modéliser la détection des drogues et le diagnostic à l'aide du "programme d'évaluation et de classification des drogues")
Auteur(s) :
SCHECHTMAN E. ;
SHINAR D.
Année :
2005
Page(s) :
852-861
Langue(s) :
Français
Refs biblio. :
8
Domaine :
Plusieurs produits / Several products
Discipline :
PRO (Produits, mode d'action, méthode de dépistage / Substances, action mode, screening methods)
Thésaurus mots-clés
DEPISTAGE
;
ALPRAZOLAM
;
DIAGNOSTIC
;
CONDUITE DE VEHICULE
;
CANNABIS
;
AMPHETAMINE
;
CODEINE
;
POLICE
;
MODELE
Note générale :
Accident, Analysis and Prevention, 2005, 37, (7), 852-861
Note de contenu :
tabl.
Résumé :
ENGLISH :
In this study, we propose formal models and algorithms to detect drug impairment and identify the impairing drug type, on the basis of data obtained by a Drug Evaluation and Classification (DEC) investigation. The DEC program relies on measurements of vital signs and observable signs and symptoms. A formal model, based on data collected by police officers trained to detect and identify drug impairments, yielded sensitivity levels greater than 60% and specificity levels greater than 90% for impairments caused by cannabis, alprazolam, and amphetamine. For codeine, with a specificity of nearly 90% the sensitivity was only 20%. Using logistic regression, the formal model was much more accurate than the trained officers in identifying impairments from cannabis, alprazolam, and amphetamine. Both the formal model and the officers were quite poor in identifying codeine impairment. In conclusion, the joint application of the DECP procedures with the formal model is useful for drug detection and identification. (Author' s abstract)
Affiliation :
Industrial Engineering and Management, Ben Gurion University of the Negev, Ben Gurion Blvd, Beer Sheva 84105. Email : shinar@bgu.ac.il
Israël. Israel.
Israël. Israel.
Cote :
A02387