MoDE: Improving Mixture of Depression Experts With Mutual Information Estimator for Depression Detection

The clinical interview dialogues is a critical approach in diagnosing depression. Existing methods have achieved impressive results on clinical depression interview datasets. However, they heavily rely on neural networks to automatically discover crucial question-answer pairs within clinical dialogues, lacking explicit modeling of depression factors present in clinical interviews. To fill this gap, we propose a novel mutual information-based mixture of depression experts, which explicitly analyzes depression factors within clinical interview dialogues and identify the contribution of individual and composite depression factors. Specifically, we first identify depression factors, such as social abilities, mental state, and medication history from a causal perspective. We design a Mixture of Depression Experts, consisting of multiple depression expert networks, each specialized in handling either individual or composite depression factors. In addition, we propose a mutual information-based gating function to enable dynamic depression diagnosis decisions conditioned on either individual or composite depression factors. Experiments conducted on publicly available datasets demonstrate the superiority and interpretability of our model.