ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2023
Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
24 citing papers in PubMed.
- High-Fidelity Synthetic Data Replicates Clinical Prediction Performance in a Million-Patient Diabetes Cohort.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine learning prediction of common bile duct stones using synthetic data to guide emergency ERCP decisions.Scientific reports · 2026Article
- Review
- Integrating curation into scientific publishing to train AI models.Bioinformatics (Oxford, England) · 2026Article
- Artificial intelligence in clinical trial participant recruitment and retention: A scoping review and meta-analysis.Journal of clinical and translational science · 2026Review
- The use of artificial intelligence in healthcare as perceived by the citizens and patients: a narrative review of the literature.European journal of public health · 2025Review
- Large language models in clinical trials: applications, technical advances, and future directions.BMC medicine · 2025Review
- Enhanced pre-recruitment framework for clinical trial questionnaires through the integration of large language models and knowledge graphs.Scientific reports · 2025Article
- Natural Language Processing in Support of Evidence-based Medicine: A Scoping Review.Findings of ACL. ACL · 2025Article
- Large Language Model Architectures in Health Care: Scoping Review of Research Perspectives.Journal of medical Internet research · 2025Article
- Enhancing Patient-Trial Matching With Large Language Models: A Scoping Review of Emerging Applications and Approaches.JCO clinical cancer informatics · 2025Article
- Classifying the Information Needs of Survivors of Domestic Violence in Online Health Communities Using Large Language Models: Prediction Model Development and Evaluation Study.Journal of medical Internet research · 2025Article
- Synthetic data distillation enables the extraction of clinical information at scale.NPJ digital medicine · 2025Article
- Benchmarking Large Language Models from Open and Closed Source Models to Apply Data Annotation for Free-Text Criteria in Healthcare.Future internet · 2025Article
- Survey and improvement strategies for gene prioritization with large language models.Bioinformatics advances · 2025Article
- Review
- Matching patients to clinical trials with large language models.Nature communications · 2024Article
- Article
- Exploring Large Language Models and the Metaverse for Urologic Applications: Potential, Challenges, and the Path Forward.International neurourology journal · 2024Article
- Large Language Models for Intraoperative Decision Support in Plastic Surgery: A Comparison between ChatGPT-4 and Gemini.Medicina (Kaunas, Lithuania) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
The process of matching patients with suitable clinical trials is essential for advancing medical research and providing optimal care. However, current approaches face challenges such as data standardization, ethical considerations, and a lack of interoperability between Electronic Health Records (EHRs) and clinical trial criteria. In this paper, we explore the potential of large language models (LLMs) to address these challenges by leveraging their advanced natural language generation capabilities to improve compatibility between EHRs and clinical trial descriptions. We propose an innovative privacy-aware data augmentation approach for LLM-based patient-trial matching (LLM-PTM), which balances the benefits of LLMs while ensuring the security and confidentiality of sensitive patient data. Our experiments demonstrate a 7.32% average improvement in performance using the proposed LLM-PTM method, and the generalizability to new data is improved by 12.12%. Additionally, we present case studies to further illustrate the effectiveness of our approach and provide a deeper understanding of its underlying principles.
Indexed as
Identifiers
38222339PMC10785941What OpenQuestion holds
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.