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This article shows how an AI assistant can reduce much of the exploratory work involved in discovering and modeling of public SDoH data. An open-source AI assistant will be highlighted that integrates several of the most widely used public data sources. The assistant can help find relevant variables, understand geographic and temporal coverage, and compare related measures across sources before building statistical, machine learning, or AI pipelines.
Agents are playing an increasing role in Social Determinants of Health (SDoH), especially in urban planning, public health, and social support organizations, as they monitor data, reason through problems, and act across complex systems.
This article investigates the performance of models on social media posts by examining different Transformer architectures. It evaluates the effectiveness of fine-tuning and prompt-based methods, aiming to identify best practices and trade-offs through performance comparisons across various models.