ArticleJAMIA open2025
LLM-IE: a python package for biomedical generative information extraction with large language models.
Article in JAMIA open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Hierarchical prompting with reasoning large language models for immune checkpoint inhibitor-associated myocarditis evidence extraction pilot study.European heart journal. Digital health · 2026Article
- OmniExtract: an automatic data extraction tool based on large language model and prompt engineering.Briefings in bioinformatics · 2026Article
- Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration.CPT: pharmacometrics & systems pharmacology · 2026Article
- Structured Schemas for Provenance-Rich, LLM-Assisted QSP Model Calibration.bioRxiv : the preprint server for biology · 2026Article
- Information extraction from clinical notes: are we ready to switch to large language models?Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Curation at Scale with EPITOME: Extraction Pipeline for Immunological Texts and Open-Source Multimodal Enquiry.bioRxiv : the preprint server for biology · 2026Article
- MetaphorPrompt2-A Structure and Function-Focused Approach for Extracting Causal Events from Biological Text.Computational and structural biotechnology journal · 2026Article
- Information Extraction of Doctoral Theses Using Two Different Large Language Models vs Health Services Researchers: Development and Usability Study.JMIR formative research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Objectives: Despite the recent adoption of large language models (LLMs) for biomedical information extraction (IE), challenges in prompt engineering and algorithms persist, with no dedicated software available. To address this, we developed Materials and Methods: The Results: The sentence-based prompting algorithm resulted in the best 8-shot performance of over 70% strict F1 for entity extraction and about 60% F1 for entity attribute extraction. Discussion: We developed a Python package, Conclusion: The
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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.