ArticleJournal of the American Medical Informatics Association : JAMIA2025
RAMIE: retrieval-augmented multi-task information extraction with large language models on dietary supplements.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Automated identification of keratinocyte cancers in pathology reports using large language models.PLOS digital health · 2026Article
- RescueGPT: An Automated System for Detecting Adverse Safety Events in Prehospital Emergency Medical Service Notes With a Zero-Shot Approach With Large Language Models: A Proof-of-Concept Study.Learning health systems · 2026Article
- Assessing Multimodal AI for Visual Information Extraction of Pharmacology.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- EPEE: towards efficient and effective foundation models in biomedicine.npj health systems · 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
- Uncertainty-aware large language models for explainable disease diagnosis.NPJ digital medicine · 2025Article
- MMRAG: multi-mode retrieval-augmented generation with large language models for biomedical in-context learning.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Improving Dietary Supplement Information Retrieval: Development of a Retrieval-Augmented Generation System With Large Language Models.Journal of medical Internet research · 2025Article
- Large language models for disease diagnosis: a scoping review.NPJ artificial intelligence · 2025Review
- Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
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4 authors.
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Abstract
objectiveTo develop an advanced multi-task large language model (LLM) framework for extracting diverse types of information about dietary supplements (DSs) from clinical records.
methodsWe focused on 4 core DS information extraction tasks: named entity recognition (2 949 clinical sentences), relation extraction (4 892 sentences), triple extraction (2 949 sentences), and usage classification (2 460 sentences). To address these tasks, we introduced the retrieval-augmented multi-task information extraction (RAMIE) framework, which incorporates: (1) instruction fine-tuning with task-specific prompts; (2) multi-task training of LLMs to enhance storage efficiency and reduce training costs; and (3) retrieval-augmented generation, which retrieves similar examples from the training set to improve task performance. We compared the performance of RAMIE to LLMs with instruction fine-tuning alone and conducted an ablation study to evaluate the individual contributions of multi-task learning and retrieval-augmented generation to overall performance improvements.
resultsUsing the RAMIE framework, Llama2-13B achieved an F1 score of 87.39 on the named entity recognition task, reflecting a 3.51% improvement. It also excelled in the relation extraction task with an F1 score of 93.74, a 1.15% improvement. For the triple extraction task, Llama2-7B achieved an F1 score of 79.45, representing a significant 14.26% improvement. MedAlpaca-7B delivered the highest F1 score of 93.45 on the usage classification task, with a 0.94% improvement. The ablation study highlighted that while multi-task learning improved efficiency with a minor trade-off in performance, the inclusion of retrieval-augmented generation significantly enhanced overall accuracy across tasks.
conclusionThe RAMIE framework demonstrates substantial improvements in multi-task information extraction for DS-related data from clinical records.
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