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Found 5 papers, 3 papers with code
Extracting Social Determinants of Health from Pediatric Patient Notes Using Large Language Models: Novel Corpus and Methods
In this work, we present a novel annotated corpus, the Pediatric Social History Annotation Corpus (PedSHAC), and evaluate the automatic extraction of detailed SDoH representations using fine-tuned and in-context learning methods with Large Language Models (LLMs).
Prompt-based Extraction of Social Determinants of Health Using Few-shot Learning
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.
LeafAI: query generator for clinical cohort discovery rivaling a human programmer
Conclusions: Our work contributes a state-of-the-art data model-agnostic query generation system capable of conditional reasoning using a knowledge base.
Leveraging Natural Language Processing to Augment Structured Social Determinants of Health Data in the Electronic Health Record
In an EHR case study, we applied the SDOH extractor to a large clinical data set with 225, 089 patients and 430, 406 notes with social history sections and compared the extracted SDOH information with existing structured data.
The Leaf Clinical Trials Corpus: a new resource for query generation from clinical trial eligibility criteria
In order to identify potential participants at scale, these criteria must first be translated into queries on clinical databases, which can be labor-intensive and error-prone.
