Author(s): Murch WS; Kairouz S; Dauphinais S; Picard E; Costes JM; French M;

Background and aims: Participating in online gambling is associated with an increased risk for experiencing gambling-related harms, driving calls for more effective, personalized harm prevention initiatives. Such initiatives depend on the development of models capable of detecting at-risk online gamblers. We aimed to determine whether machine learning alg ...

Article GUID: 36880253