A systematic review of AI for depressive disorder detection that introduces a novel hierarchical taxonomy organized by clinical task, data modality, and model class.
Strong transitivity relations and graph neural networks
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2025 2representative citing papers
PBiLoss is a model-agnostic regularization loss with PopPos and PopNeg sampling that reduces popularity bias metrics PRU and PRI by up to 10% in GNN recommenders while preserving accuracy on datasets like MovieLens.
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AI Models for Depressive Disorder Detection and Diagnosis: A Review
A systematic review of AI for depressive disorder detection that introduces a novel hierarchical taxonomy organized by clinical task, data modality, and model class.
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PBiLoss: Popularity-Aware Regularization to Improve Fairness in Graph-Based Recommender Systems
PBiLoss is a model-agnostic regularization loss with PopPos and PopNeg sampling that reduces popularity bias metrics PRU and PRI by up to 10% in GNN recommenders while preserving accuracy on datasets like MovieLens.