ArticleNPJ digital medicine2025
Clinical entity augmented retrieval for clinical information extraction.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
- SPELL: A scalable NLP method using regular expressions and large language models for clinical information extraction.Computer methods and programs in biomedicine · 2026Article
- Semantic Similarity Search Approach to Extract Exemplars of Stigmatizing and Positive Language in Obstetric Clinical Notes: Exploratory Study.JMIR medical informatics · 2026Article
- OntoCodex: a multi-agent biomedical ontology enrichment framework.npj health systems · 2026Article
- Evaluation Methods for Inference-Time Retrieval-Augmented and Graph Retrieval-Augmented Large Language Models in Health Care: Scoping Review.Journal of medical Internet research · 2026Article
- The Best of Both Worlds: How Combining a Large Language Model and a Rule-Based Algorithm Makes Catheter-Associated Urinary Tract Infection Surveillance More Efficient.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2026Article
- Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Large Language Models for Postoperative Decision Support: Comparative Analysis.Journal of medical Internet research · 2026Article
- Prognostic data extraction harnessing a privacy-preserving large language model: a clinician-AI collaborative retrospective evaluation in head and neck oncology.NPJ precision oncology · 2026Article
- A real-world feasibility evaluation of LLM-based clinical prediction: emergency department return visit admission across two academic medical centers.Research square · 2026Article
- Machine learning models to detect opioid misuse in emergency department patients at triage.The American journal of emergency medicine · 2026Article
- Longitudinal Language-Model Reasoning Enables Automated Labeling of Lung Cancer Recurrence from Unstructured Clinical Records.Research square · 2026Article
- PrecLLM: A Privacy-Preserving Framework for Efficient Clinical Annotation Extraction from Unstructured EHRs using Small-Scale LLMs.Research square · 2026Article
- Fourier Kolmogorov-Arnold Network integrated into BioBERT-based model for Biomedical Named Entity Recognition.NPJ digital medicine · 2026Article
- Holistic evaluation of large language models for medical tasks with MedHELM.Nature medicine · 2026Article
- Integrating Fine-Tuning and Retrieval-Augmented Generation for Healthcare AI Systems: A Scoping Review.Bioengineering (Basel, Switzerland) · 2026Review
- Article
- A multi-agent GraphRAG framework for pharmacotherapy safety verification in clinical decision support systems.Frontiers in medicine · 2026Article
- CLIN-LLM: A safety-constrained hybrid framework for clinical diagnosis and treatment generation.PloS one · 2026Article
- Inference Gap in Domain Expertise and Machine Intelligence in Named Entity Recognition: Creation of and Insights from a Substance Use-related Dataset.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
- A roadmap for medical large language models: a review of foundations, applications, and challenges.Military Medical Research · 2026Review
- Two-stage prompting framework with predefined verification steps for evaluating diagnostic reasoning tasks on two datasets.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Large language models (LLMs) with retrieval-augmented generation (RAG) have improved information extraction over previous methods, yet their reliance on embeddings often leads to inefficient retrieval. We introduce CLinical Entity Augmented Retrieval (CLEAR), a RAG pipeline that retrieves information using entities. We compared CLEAR to embedding RAG and full-note approaches for extracting 18 variables using six LLMs across 20,000 clinical notes. Average F1 scores were 0.90, 0.86, and 0.79; inference times were 4.95, 17.41, and 20.08 s per note; average model queries were 1.68, 4.94, and 4.18 per note; and average input tokens were 1.1k, 3.8k, and 6.1k per note for CLEAR, embedding RAG, and full-note approaches, respectively. In conclusion, CLEAR utilizes clinical entities for information retrieval and achieves >70% reduction in token usage and inference time with improved performance compared to modern methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.