ArticleJournal of the American Medical Informatics Association : JAMIA2024
RT: a Retrieving and Chain-of-Thought framework for few-shot medical named entity recognition.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed.
- The Rise of Small Language Models in Healthcare: A Comprehensive Survey.Computer science review · 2026Article
- Symptom Terminology Normalization in Traditional Chinese Medicine: Development and Evaluation of a 2-Stage Deep Learning Framework Based on Fine-Grained Semantic Classification.JMIR medical informatics · 2026Article
- RadCoT: a Radiological Chain-of-Thought framework for enhanced error detection in radiology reports.European radiology experimental · 2026Article
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- Domain-specific versus general large language models: a review and empirical benchmark in real medical texts.Scientific reports · 2026Review
- Supervised Fine-Tuning of Large Language Models With Chain-of-Thought Reasoning for Pediatric Heart Disease Detection in Unstructured Echocardiogram Reports: Algorithm Development and Validation.JMIR formative research · 2026Article
- Open-Source Large Language Models Distilled DeepSeek-R1 Pose Challenges for On-Premises Clinical Deployment in Medical Diagnosis: A Comparative Study of Performance.Journal of medical systems · 2026Article
- Benchmarking large language models for pathogen-disease classification in post-acute infection syndromes.Briefings in bioinformatics · 2026Article
- A joint span-entity prediction approach with generative and cross-lingual meta-learning for low-resource Japanese NER.Scientific reports · 2026Article
- CancerLLM: a large language model in cancer domain.NPJ digital medicine · 2026Article
- Article
- Leveraging large language models for rare disease named entity recognition.PLOS digital health · 2026Article
- Automated abstraction of clinical parameters of multiple myeloma from real-world clinical notes using large language models.BMC medical informatics and decision making · 2026Article
- Integrating expert knowledge into large language models improves performance for psychiatric reasoning and diagnosis.Psychiatry research · 2026Article
- Understanding the robustness of vision-language models to medical image artefacts.NPJ digital medicine · 2025Article
- Benchmarking retrieval-augmented large language models in biomedical NLP: Application, robustness, and self-awareness.Science advances · 2025Article
- Chinese crop diseases and pests named entity recognition based on variational information bottleneck and feature enhancement.Scientific reports · 2025Article
- Large Language Model Synergy for Ensemble Learning in Medical Question Answering: Design and Evaluation Study.Journal of medical Internet research · 2025Article
- Language Models for Multilabel Document Classification of Surgical Concepts in Exploratory Laparotomy Operative Notes: Algorithm Development Study.JMIR medical informatics · 2025Article
- Dynamic few-shot prompting for clinical note section classification using lightweight, open-source large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
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Abstract
objectivesThis article aims to enhance the performance of larger language models (LLMs) on the few-shot biomedical named entity recognition (NER) task by developing a simple and effective method called Retrieving and Chain-of-Thought (RT) framework and to evaluate the improvement after applying RT framework. MATERIALS AND
methodsGiven the remarkable advancements in retrieval-based language model and Chain-of-Thought across various natural language processing tasks, we propose a pioneering RT framework designed to amalgamate both approaches. The RT approach encompasses dedicated modules for information retrieval and Chain-of-Thought processes. In the retrieval module, RT discerns pertinent examples from demonstrations during instructional tuning for each input sentence. Subsequently, the Chain-of-Thought module employs a systematic reasoning process to identify entities. We conducted a comprehensive comparative analysis of our RT framework against 16 other models for few-shot NER tasks on BC5CDR and NCBI corpora. Additionally, we explored the impacts of negative samples, output formats, and missing data on performance.
resultsOur proposed RT framework outperforms other LMs for few-shot NER tasks with micro-F1 scores of 93.50 and 91.76 on BC5CDR and NCBI corpora, respectively. We found that using both positive and negative samples, Chain-of-Thought (vs Tree-of-Thought) performed better. Additionally, utilization of a partially annotated dataset has a marginal effect of the model performance. DISCUSSION: This is the first investigation to combine a retrieval-based LLM and Chain-of-Thought methodology to enhance the performance in biomedical few-shot NER. The retrieval-based LLM aids in retrieving the most relevant examples of the input sentence, offering crucial knowledge to predict the entity in the sentence. We also conducted a meticulous examination of our methodology, incorporating an ablation study.
conclusionThe RT framework with LLM has demonstrated state-of-the-art performance on few-shot NER tasks.
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