Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive SymptomsDownload PDF

Published: 28 Jan 2022, Last Modified: 13 Feb 2023ICLR 2022 PosterReaders: Everyone
Keywords: Graph neural networks sentiment analysis node-embedding algorithm diagnostic prediction task
Abstract: 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 captures 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. Moreover, we also evaluate the performance of our novel model on a Twitter sentiment dataset. We show that our model outperforms a general GNN model by leveraging our novel 2-D node attributes. These results demonstrate the generality of the proposed method.
One-sentence Summary: We encode word embeddings by using graph representation learning method, which schematizes context-level depressive features for depression state prediction.
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