Predicting the presence of major depressive disorder (MDD) using behavioural and cognitive signals is a highly non-trivial task. The heterogeneous clinical profile of MDD means that any given speech, facial expression and/or observed cognitive pattern may be associated with a unique combination of depressive symptoms. Conventional discriminative machine learning models potentially lack the complexity to robustly model this heterogeneity. Bayesian networks, however, may instead be well-suited to such a scenario. These networks are probabilistic graphical models that efficiently describe the joint probability distribution over a set of random variables by explicitly capturing their conditional dependencies. This framework provides further advantages over standard discriminative modelling by offering the possibility to incorporate expert opinion in the graphical structure of the models, generating explainable model predictions, informing about the uncertainty of predictions, and naturally handling missing data. In this study, we apply a Bayesian framework to capture the relationships between depression, depression symptoms, and features derived from speech, facial expression and cognitive game data collected at thymia.

翻译：利用行为与认知信号预测重度抑郁障碍（MDD）的存在是一项极具挑战性的任务。MDD临床特征的异质性意味着，任何特定的语音、面部表情和/或观察到的认知模式都可能与独特的抑郁症状组合相关联。传统的判别式机器学习模型可能缺乏稳健建模这种异质性的复杂性。然而，贝叶斯网络可能更适合此类场景。这些网络是概率图模型，通过显式捕获随机变量之间的条件依赖关系，高效地描述其联合概率分布。相较于标准判别式建模，该框架具有更多优势：可在模型的图结构中整合专家意见、生成可解释的模型预测、揭示预测的不确定性，并自然处理缺失数据。在本研究中，我们应用贝叶斯框架来捕捉抑郁、抑郁症状与从thymia平台收集的语音、面部表情及认知游戏数据中提取的特征之间的关系。