LLMs only become competitive at spatial data integration when given pre-computed geometric features; a review-and-refine prompt then exceeds hand-tuned heuristics.
Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning
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abstract
Social determinants of health (SDOH) documented in the electronic health record through unstructured text are increasingly being studied to understand how SDOH impacts patient health outcomes. In this work, we utilize the Social History Annotation Corpus (SHAC), a multi-institutional corpus of de-identified social history sections annotated for SDOH, including substance use, employment, and living status information. We explore the automatic extraction of SDOH information with SHAC in both standoff and inline annotation formats using GPT-4 in a one-shot prompting setting. We compare GPT-4 extraction performance with a high-performing supervised approach and perform thorough error analyses. Our prompt-based GPT-4 method achieved an overall 0.652 F1 on the SHAC test set, similar to the 7th best-performing system among all teams in the n2c2 challenge with SHAC.
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Can Large Language Models Integrate Spatial Data? Empirical Insights into Reasoning Strengths and Computational Weaknesses
LLMs only become competitive at spatial data integration when given pre-computed geometric features; a review-and-refine prompt then exceeds hand-tuned heuristics.