Bayesian Networks for the robust and unbiased prediction of depression and its symptoms utilizing speech and multimodal data

TL;DR

This paper explores the use of Bayesian networks to improve the prediction of depression and its symptoms from multimodal data, offering a robust, explainable, and uncertainty-aware approach that handles data heterogeneity and missing information.

Contribution

The study introduces a Bayesian network framework for depression prediction that incorporates multimodal data and expert knowledge, enhancing robustness and interpretability over traditional models.

Findings

Bayesian networks effectively model complex relationships between depression and symptoms.

The approach handles missing data and provides uncertainty estimates.

Explainability of predictions is improved through the graphical model structure.

Abstract

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.