PLOS
open access
CC BY 4.0
SYNERGY-VAE: An explainable generative deep learning framework for discovering depression subgroups from multimodal population health data
Depression represents a complex, multifactorial disorder influenced by the interplay of biological, behavioral, and social determinants. While extensive health surveys, including the National Health and Nutrition Examination Survey (NHANES), gather comprehensive multidomain data, existing studies t
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Expresso-AI: Explainable Video-Based Deep Learning Models for Depression Diagnosis#307041
- PSYCHE-D: predicting change in depression severity using person-generated health data#374653
- The benefits and future potential of generative artificial intelligence (GAI) on mental health: a Delphi study.#9578
- Study on the Antidepressant Effects and Mechanism of Action of Cang-ai Volatile Oil Based on Near-Infrared Functional Brain Imaging and the NT-Trk Signalling Pathway#871260
- Applying machine-learning and deep-learning to predict depression from brain MRI and identify depression-related brain biology#8299
Record · ID 1081358
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive