mlsanityData to 14 Oct 2026

Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching

arXiv 2303.16756 ↗First posted 24 Mar 2023cs.CL cs.AIcited by 30

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Abstract (arXiv)

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.

Key facts. Cited by 30 papers in the map (data through 14 Oct 2026). Builds on 17 of its 36 references that are in the map. First posted on arXiv 24 Mar 2023.

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17 of 36 references.

References · 17

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5 more
  1. 2016Counter-fitting Word Vectors to Linguistic Constraintscited by 140
  2. 2020COMPOSE: Cross-Modal Pseudo-Siamese Network for Patient Trial Matchingcited by 37
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Citing papers per year: 2023: 3; 2024: 14; 2025: 13.

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  1. 2024KIVI: A Tuning-Free Asymmetric 2bit Quantization for KV Cachecited by 637
  2. 2023Matching Patients to Clinical Trials with Large Language Modelscited by 69
  3. 2024KV Cache Compression, But What Must We Give in Return? A Comprehensive Benchmark of Long Context Capable Approachescited by 42
  4. 2024Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challengescited by 30
  5. 2024Data-Centric Foundation Models in Computational Healthcare: A Surveycited by 27
  6. 2024Assessing and Enhancing Large Language Models in Rare Disease Question-answeringcited by 16
  7. 2024DHP Benchmark: Are LLMs Good NLG Evaluators?cited by 12
  8. 2024Panacea: A foundation model for clinical trial search, summarization, design, and recruitmentcited by 10
  9. 2024Digital Diagnostics: The Potential Of Large Language Models In Recognizing Symptoms Of Common Illnessescited by 8
  10. 2024Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental Healthcited by 5
  11. 2025Measuring Diversity in Synthetic Datasetscited by 5
  12. 2025Cocktail: Chunk-Adaptive Mixed-Precision Quantization for Long-Context LLM Inferencecited by 5
18 more
  1. 2024Aligning Actions and Walking to LLM-Generated Textual Descriptionscited by 3
  2. 2025Large Language Models for Healthcare Text Classification: A Systematic Reviewcited by 3
  3. 2025ChemActor: Enhancing Automated Extraction of Chemical Synthesis Actions with LLM-Generated Datacited by 3
  4. 2023A ML-LLM pairing for better code comment classificationcited by 2
  5. 2024Data Augmentation of Multi-turn Psychological Dialogue via Knowledge-driven Progressive Thought Promptingcited by 2
  6. 2024Rephrasing Electronic Health Records for Pretraining Clinical Language Modelscited by 2
  7. 2024How Private are Language Models in Abstractive Summarization?cited by 2
  8. 2025Clinical trial cohort selection using Large Language Models on n2c2 Challengescited by 2
  9. 2025Survey and Improvement Strategies for Gene Prioritization with Large Language Modelscited by 2
  10. 2025Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMscited by 2
  11. 2025Natural Language Processing in Support of Evidence-based Medicine: A Scoping Reviewcited by 2
  12. 2025A Survey on LLM-Assisted Clinical Trial Recruitmentcited by 2
  13. 2023Integrating curation into scientific publishing to train AI modelscited by 1
  14. 2024On The Role of Prompt Construction In Enhancing Efficacy and Efficiency of LLM-Based Tabular Data Generationcited by 1
  15. 2025Evaluating the effectiveness of LLM-based interoperabilitycited by 1
  16. 2025CLaDMoP: Learning Transferrable Models from Successful Clinical Trials via LLMscited by 0
  17. 2025A systematic review of trial-matching pipelines using large language modelscited by 0
  18. 2025Mitigating Semantic Drift: Evaluating LLMs' Efficacy in Psychotherapy through MI Dialogue Summarizationcited by 0