Personality biomarkers of pathological gambling: A machine learning study

Journal of Neuroscience Methods
Antonio CerasaAldo Quattrone

Abstract

The application of artificial intelligence to extract predictors of Gambling disorder (GD) is a new field of study. A plethora of studies have suggested that maladaptive personality dispositions may serve as risk factors for GD. Here, we used Classification and Regression Trees algorithm to identify multivariate predictive patterns of personality profiles that could identify GD patients from healthy controls at an individual level. Forty psychiatric patients, recruited from specialized gambling clinics, without any additional comorbidity and 160 matched healthy controls completed the Five-Factor model of personality as measured by the NEO-PI-R, which were used to build the classification model. Classification algorithm was able to discriminate individuals with GD from controls with an AUC of 77.3% (95% CI 0.65-0.88, p<0.0001). A multidimensional construct of traits including sub-facets of openness, neuroticism and conscientiousness was employed by algorithm for classification detection. To the best of our knowledge, this is the first study that combines behavioral data with machine learning approach useful to extract multidimensional features characterizing GD realm. Our study provides a proof-of-concept demonstrating the poten...Continue Reading

Citations

Nov 13, 2019·Addiction Biology·Alexander GenauckNina Romanczuk-Seiferth
Dec 6, 2018·Italian Journal of Pediatrics·Pietro FerraraGiovanni Corsello
Nov 6, 2018·European Psychiatry : the Journal of the Association of European Psychiatrists·Gemma Mestre-BachSusana Jiménez-Murcia

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