Monaro Merylin, Toncini Andrea, Ferracuti Stefano, Tessari Gianmarco, Vaccaro Maria G, De Fazio Pasquale, Pigato Giorgio, Meneghel Tiziano, Scarpazza Cristina, Sartori Giuseppe
Department of General Psychology, University of Padova, Padova, Italy.
Department of Human Neurosciences, University of Roma "La Sapienza", Rome, Italy.
Front Psychiatry. 2018 Jun 8;9:249. doi: 10.3389/fpsyt.2018.00249. eCollection 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 insurance companies. Critically, depression is potentially easily malingered, as the symptoms that characterize this psychiatric disorder are not difficult to emulate. Although some tools to assess malingering of psychiatric conditions are already available, they are principally based on self-reporting and are thus easily 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 60 subjects (20 depressed patients and 40 healthy volunteers); the second one, which was used to test the machine-learning models, comprises 27 subjects (9 depressed patients and 18 healthy volunteers). In both 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 reported a higher number of depressive and non-depressive symptoms than depressed participants, whereas individuals suffering from depression took more time to perform the mouse-based tasks 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. Despite this, the data are not conclusive, as the accuracy of the algorithm has not been compared with the accuracy of the clinicians; this study presents a possible useful method that is worth further investigation.
重度抑郁症是一种高发性精神疾病，对社会经济有着重大影响，包括直接成本和间接成本。为了从保险公司获得经济赔偿，重度抑郁症症状可能会被伪造或夸大。至关重要的是，抑郁症很容易被伪装，因为这种精神障碍的特征症状并不难模仿。尽管已经有一些评估精神疾病伪装的工具，但它们主要基于自我报告，因此很容易被伪造。在本文中，我们提出了一种自动检测抑郁症伪装的新方法，该方法基于对患者在执行双选计算机任务时鼠标移动的分析，患者需要回答关于抑郁症状的简单和复杂问题。这个工具显然比其他工具有一个关键优势：运动学动作不受受试者有意识控制，因此几乎不可能欺骗。本研究招募了两组受试者。第一组用于训练不同的机器学习算法，由60名受试者组成（20名抑郁症患者和40名健康志愿者）；第二组用于测试机器学习模型，由27名受试者组成（9名抑郁症患者和18名健康志愿者）。在两组中，健康志愿者被随机分配到说谎者和说实话者组。机器学习模型根据受试者反应期间收集的鼠标动态特征以及参与者报告的症状数量进行训练。统计结果表明，伪装抑郁症的个体报告的抑郁和非抑郁症状数量比抑郁症患者多，而与说实话者和说谎者相比，抑郁症患者完成基于鼠标的任务花费的时间更多。机器学习模型在区分说谎者与抑郁症患者和说实话者方面的分类准确率高达96%。尽管如此，数据并不具有决定性，因为该算法的准确性尚未与临床医生的准确性进行比较；本研究提出了一种可能有用的方法，值得进一步研究。
