AI-driven Multimodal Representation Learning for Latent Mediation Structure Discovery of Socioeconomic Disadvantage, Psychosocial Factors, and Cardiometabolic Multimorbidity: Insights from the All of Us Research Program
arXiv:2608.04016v1 Announce Type: new Abstract: Social disadvantage is associated with multimorbidity, but the pathways linking social conditions to disease burden remain poorly understood. We developed an AI-driven multimodal mediation framework that integrates socioeconomic, psychosocial, clinical, laboratory, behavioral, and genomic data from the All of Us Research Program. Modality-specific variational autoencoders were used to derive latent representations of each data domain, and mediation
Record details
Published: 6 August 2026
Source: arXiv cs.CY
Category: Research
Topics: Healthcare · Biotech
Retrieved: 6 August 2026
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ethics.ai (6 August 2026), “AI-driven Multimodal Representation Learning for Latent Mediation Structure Discovery of Socioeconomic Disadvantage, Psychosocial Factors, and Cardiometabolic Multimorbidity: Insights from the All of Us Research Program,” evidence record 16633, https://ethics.ai/record/16633 (originally published by arXiv cs.CY).
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