Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data
This work addresses the problem of improving diagnostic accuracy for depression in clinical settings, but it appears incremental as it applies an existing Bayesian method to new multimodal data without claiming major breakthroughs.
The study tackled the challenge of predicting major depressive disorder (MDD) and its symptoms using speech, facial expression, and cognitive data by applying Bayesian networks to model the heterogeneity and conditional dependencies in these signals, resulting in a framework that offers explainable predictions, uncertainty quantification, and robust handling of missing data.
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.