Article

REFINE: Closing the Loop Between Large Language Models and Symbolic Rules in Clinical NLP

2026-08-17

Abstract excerpt

Symbolic clinical natural language processing (NLP) systems remain widely used for extracting clinical concepts from electronic health record (EHR) narratives, but maintaining rule resources requires extensive manual error analysis and rule refinement. This study investigates whether large language models (LLMs) can assist in identifying extraction errors and generating candidate rules to improve symbolic clinical...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 19e4089f-bbc6-55cd-bf46-59f21655386c
DOI: 10.64898/2026.08.11.26360118

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

REFINE: Closing the Loop Between Large Language Models and Symbolic Rules in Clinical NLPDOI 10.64898/2026.08.11.26360118Read

On the limitations of large language models in clinical diagnosisSemantic proximity Read

Large-Scale Psychiatric Concept Extraction from Electronic Health Records: A Comparative Study of Encoder-Based Language ModelsSemantic proximity Read

A Human-in-the-Loop Large Language Model System Based on the Model Context Protocol for Differential Diagnosis from Electronic Medical Records and LiteratureSemantic proximity Read

A retrieval-augmented generation large language model framework for accurate dementia identification from electronic health recordsSemantic proximity Read

Evaluation of Large Language Model Performance on the Biomedical Language Understanding and Reasoning Benchmark: Comparative StudySemantic proximity Read

CARDBiomedBench: A Benchmark for Evaluating Large Language Model Performance in Biomedical ResearchSemantic proximity Read

Applying natural language processing and large language models to clinical notes for phenotyping and diagnosing rare diseases: a systematic review.Semantic proximity Read

Enhancing LLM Accuracy and Reducing Hallucinations using Query Refinement Technique and Knowledge GraphsSemantic proximity Read

Characterizing Dementia Phenotypes from Unstructured EHR Notes with Generative AI and Interpretable Machine LearningSemantic proximity Read

CD-Tron: Leveraging Large Clinical Language Model for Early Detection of Cognitive Decline from Electronic Health RecordsSemantic proximity Read

General-purpose large language models can achieve physician-level accuracy in complex medical data extractionSemantic proximity Read

Performance Characteristics of Reasoning Large Language Models for Evidence Extraction from Clinical Genomics LiteratureSemantic proximity Read