The Detection of Malingering: A New Tool to Identify Made-Up Depression - Monaro, Merylin; Toncini, Andrea; Ferracuti, Stefano; Tessari, Gianmarco; Vaccaro, Maria G.; De Fazio, Pasquale; Pigato, Giorgio; Meneghel, Tiziano; Scarpazza, Cristina; Sartori, Giuseppe (Frontiers in Psychiatry, 2018)
Major depression is a high prevalence mental disease with major socio-economic impact, for both the direct and the indirect costs. Major depression symptoms can be faked or exaggerated in order to obtain economic compensation from insurances. Critically, depression is very easily malingered as the symptoms that characterize this psychiatric disorder are easy to emulate. Although some tools to assess malingering of psychiatric conditions are already available, they are principally based on self-reporting and thus, simple to be faked. In this paper, we propose a new method to automatically detect the simulation of depression, which is based on the analysis of mouse movements while the patient is engaged in a double choice computerized task, responding to simple and complex questions about depressive symptoms. This tool clearly has a key advantage over the other tools: the kinematic movement is not consciously controllable by the subjects, and thus it is almost impossible to deceive. Two groups of subjects were recruited for the study. The first one, which was used to train different machine learning algorithms, comprises of 60 subjects (20 depressed patients and 40 healthy volunteers); the second one, which was used to test the machine learning models, comprises of 27 subjects (9 depressed patients and 18 healthy volunteers). In both the groups, the healthy volunteers were randomly assigned to the liars and truth-tellers group. Machine learning models were trained on mouse dynamics features, which were collected during the subject response, and on the number of symptoms reported by participants. Statistical results demonstrated that individuals that malingered depression declared to experience a higher number of depressive and not depressive symptoms respect to depressed participants, whereas really depressed subjects took more time than to perform the mouse-based task compared to both truth-tellers and liars. Machine learning models reached a classification accuracy up to 96% in distinguishing liars from depressed patients and truth-tellers.