ArticleHealthcare informatics research2025
Towards Efficient Patient Recruitment for Clinical Trials: Application of a Prompt-Based Learning Model.
Article in Healthcare informatics research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Large Language Models in Clinical Trial Recruitment: Sociotechnical and Economic Framework Development Study.JMIR AI · 2026Article
- Enhancing Patient-Trial Matching With Large Language Models: A Scoping Review of Emerging Applications and Approaches.JCO clinical cancer informatics · 2025Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAll clinical trials face a significant bottleneck in identifying eligible participants, particularly due to the complexity of unstructured medical texts. Recent advances in natural language processing, especially the advent of transformer-based models, have shown promise in this domain. In this study, we evaluated the performance of a prompt-based large language model (LLM) for cohort selection from unstructured medical notes.
methodsMedical records were annotated with Med- CAT using the Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) ontology. For each trial eligibility criterion, we extracted sentences containing relevant annotated concepts through an ontology-driven summarization process. These summaries were then input into a prompt-based LLM (GPT-3.5-turbo), tasked with classifying eligibility criteria in a zero-shot setting. Model performance was assessed using the 2018 National Natural Language Processing Clinical Challenges (n2c2) dataset, which required the classification of 288 patients' medical records according to 13 eligibility criteria.
resultsThe proposed prompt-based model achieved overall micro and macro F-measures of 0.9061 and 0.8060, respectively-among the highest scores reported for this dataset.
conclusionsOur results demonstrate that integrating ontology-based extractive summarization with prompt-based LLMs can substantially improve eligibility classification. The summarization step enhanced model focus and interpretability, particularly for long or ambiguous narratives. This pipeline offers a scalable and adaptable framework for clinical trial automation and has the potential for real-world integration with electronic medical record matching systems.
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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.