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

arXiv: https://arxiv.org/abs/2211.04924 · HTML: https://mlsanity.com/paper/2211.04924/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 9 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro](https://mlsanity.com/paper/1912.11554/) (2019)
- [Bayesian Networks in Healthcare: Distribution by Medical Condition](https://mlsanity.com/paper/2002.00224/) (2020)
- [A Comprehensive Scoping Review of Bayesian Networks in Healthcare: Past, Present and Future](https://mlsanity.com/paper/2002.08627/) (2020)
- [Speech and the n-Back task as a lens into depression. How combining both may allow us to isolate different core symptoms of depression](https://mlsanity.com/paper/2204.00088/) (2022)
- [AugmentedPCA: A Python Package of Supervised and Adversarial Linear Factor Models](https://mlsanity.com/paper/2201.02547/) (2022)

## Built on by (most cited first, 3 of 3)

- [Self-Supervised Embeddings for Detecting Individual Symptoms of Depression](https://mlsanity.com/paper/2406.17229/) (2024)
- [Speech-based Clinical Depression Screening: An Empirical Study](https://mlsanity.com/paper/2406.03510/) (2024)
- [A multimodal Bayesian Network for symptom-level depression and anxiety prediction from voice and speech data](https://mlsanity.com/paper/2512.07741/) (2025)

## Abstract (arXiv)

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.