Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms
Graph neural networks (GNNs) are widely used in regression and classification problems applied to text, in areas such as sentiment analysis and medical decision-making processes. We propose a novel form for node attributes within a GNN based model that capture node-specific embeddings for every word in the vocabulary. This provides a global representation at each node, coupled with node-level updates according to associations among words in a transcript. We demonstrate the efficacy of the approach by augmenting the accuracy of measuring major depressive disorder (MDD). Prior research has sought to make a diagnostic prediction of depression levels from patient data using several modalities, including audio, video, and text. On the DAIC-WOZ benchmark, our method outperforms state-of-art methods by a substantial margin, including those using multiple modalities.
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