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.
Topic modeling, particularly with advanced tools like BERTopic, presents a promising approach for extracting insights into SDoH from unstructured text.
“AI for Good: Transforming Social Determinants of Health” “AI for Good” and “Data for Good” initiatives […]
The prevalence of diabetes has been steadily increasing for the past 30 years and is growing […]
Government agencies and civil organizations collect, consolidate and publish Social Determinants of Health (SDOH) data. Public […]
Government agencies and civil organizations publish Social Determinants of Health (SDOH) data that can be used […]
In this story we will show you how to use the GitHub extension for JupyterLab. GitHub […]
Collaboration requires good communication. Your team members need to understand work products. And you need to […]
A Docker image is a great way to get started with JupyterHub for your data science […]