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We developed a novel, interpretable multimodal classification method toidentify symptoms of mood disorders viz. depression, anxiety and anhedoniausing audio, video and text collected from a smartphone application. We usedCNN-based unimodal encoders to learn dynamic embeddings for each modality andthen combined these through a transformer encoder. We applied these methods toa novel dataset - collected by a smartphone application - on 3002 participantsacross up to three recording sessions. Our method demonstrated bettermultimodal classification performance compared to existing methods thatemployed static embeddings. Lastly, we used SHapley Additive exPlanations(SHAP) to prioritize important features in our model that could serve aspotential digital markers.

Predicting Mood Disorder Symptoms with Remotely Collected Videos Using an Interpretable Multimodal Dynamic Attention Fusion Network