mlsanityData to 14 Oct 2026

Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data

arXiv 2401.06774 ↗First posted 9 Dec 2023cs.CLcited by 26

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Abstract (arXiv)

Large language models (LLMs) can generate natural language texts for various domains and tasks, but their potential for clinical text mining, a domain with scarce, sensitive, and imbalanced medical data, is underexplored. We investigate whether LLMs can augment clinical data for detecting Alzheimer's Disease (AD)-related signs and symptoms from electronic health records (EHRs), a challenging task that requires high expertise. We create a novel pragmatic taxonomy for AD sign and symptom progression based on expert knowledge, which guides LLMs to generate synthetic data following two different directions: "data-to-label", which labels sentences from a public EHR collection with AD-related signs and symptoms; and "label-to-data", which generates sentences with AD-related signs and symptoms based on the label definition. We train a system to detect AD-related signs and symptoms from EHRs, using three datasets: (1) a gold dataset annotated by human experts on longitudinal EHRs of AD patients; (2) a silver dataset created by the data-to-label method; and (3) a bronze dataset created by the label-to-data method. We find that using the silver and bronze datasets improves the system performance, outperforming the system using only the gold dataset. This shows that LLMs can generate synthetic clinical data for a complex task by incorporating expert knowledge, and our label-to-data method can produce datasets that are free of sensitive information, while maintaining acceptable quality.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). Builds on 23 of its 91 references that are in the map. First posted on arXiv 9 Dec 2023.

Builds on

23 of 91 references.

References · 23

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11 more
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Citing papers per year: 2024: 11; 2025: 10; 2026: 5.

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  1. 2025A Survey of LLM-based Agents in Medicine: How far are we from Baymax?cited by 85
  2. 2024DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literaturecited by 50
  3. 2024Aloe: A Family of Fine-tuned Open Healthcare LLMscited by 20
  4. 20253MDBench: Medical Multimodal Multi-agent Dialogue Benchmarkcited by 16
  5. 2024Still Not Quite There! Evaluating Large Language Models for Comorbid Mental Health Diagnosiscited by 12
  6. 2025A Large-Language Model Framework for Relative Timeline Extraction from PubMed Case Reportscited by 10
  7. 2024Personalized Collaborative Fine-Tuning for On-Device Large Language Modelscited by 9
  8. 2024Unlocking LLMs: Addressing Scarce Data and Bias Challenges in Mental Healthcited by 5
  9. 2024A Systematic Review of NLP for Dementia -- Tasks, Datasets and Opportunitiescited by 4
  10. 2024DREAM: Improving Video-Text Retrieval Through Relevance-Based Augmentation Using Large Foundation Modelscited by 3
  11. 2024Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Textcited by 3
  12. 2024Disease Entity Recognition and Normalization is Improved with Large Language Model Derived Synthetic Normalized Mentionscited by 3
14 more
  1. 2025Large Language Models for Healthcare Text Classification: A Systematic Reviewcited by 3
  2. 2025A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectivescited by 3
  3. 2024Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Datacited by 2
  4. 2025Dementia Through Different Eyes: Explainable Modeling of Human and LLM Perceptions for Early Awarenesscited by 2
  5. 2025RxSafeBench: Identifying Medication Safety Issues of Large Language Models in Simulated Consultationcited by 2
  6. 2024Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Modelscited by 1
  7. 2025Embedding-Driven Diversity Sampling to Improve Few-Shot Synthetic Data Generationcited by 1
  8. 2025Enhancing LLMs for Identifying and Prioritizing Important Medical Jargons from Electronic Health Record Notes Utilizing Data Augmentation: A Comparative Studycited by 0
  9. 2025DualAlign: Generating Clinically Grounded Synthetic Datacited by 0
  10. 2026Synthetic Data for Veterinary EHR De-identification: Benefits, Limits, and Safety Trade-offs Under Fixed Computecited by 0
  11. 2026Systematic Evaluation of the Quality of Synthetic Clinical Notes Rephrased by LLMs at Million-Note Scalecited by 0
  12. 2026Generalistic or Specific Embeddings, Which is Better? An Empirical Study on Search for Clinical Coding in Non-English Languagescited by 0
  13. 2026Fair Cognitive Impairment Detection Through Unlearningcited by 0
  14. 2026Clinical Communication Processing with Models Trained on LLM-Generated Synthetic Data: A Structured Survey and Novel Application Case Studiescited by 0