ArticleProceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing2023
Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data.
Article in Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Article
- Generating Alzheimer's narratives using large language models.BMC medical informatics and decision making · 2026Article
- A Scoping Review of Synthetic Data Generation by Language Models in Biomedical Research and Application: Data Utility and Quality Perspectives.Journal of healthcare informatics research · 2026Review
- Scalable Identification of Clinically Relevant Chronic Obstructive Pulmonary Disease Documents in Large-Scale Electronic Health Record Datasets With a Lightweight Natural Language Processing Model: Retrospective Cohort Study.JMIR medical informatics · 2026Article
- The Aloe Family recipe for open and specialized healthcare LLMs.NPJ digital medicine · 2026Article
- Machine learning prediction of common bile duct stones using synthetic data to guide emergency ERCP decisions.Scientific reports · 2026Article
- Review
- Evaluating the utility of large language models for detecting and simulating language dysfunction.Frontiers in artificial intelligence · 2026Article
- Synth-SBDH: A Synthetic Dataset of Social and Behavioral Determinants of Health for Clinical Text.Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing · 2025Article
- CARE-AD: a multi-agent large language model framework for Alzheimer's disease prediction using longitudinal clinical notes.NPJ digital medicine · 2025Article
- Social determinants of health extraction from clinical notes across institutions using large language models.NPJ digital medicine · 2025Article
- A Large-Language Model Framework for Relative Timeline Extraction from PubMed Case Reports.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Review
- DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literature.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Integrating large language models in care, research, and education in multiple sclerosis management.Multiple sclerosis (Houndmills, Basingstoke, England) · 2024Review
- Introduction to Large Language Models (LLMs) for dementia care and research.Frontiers in dementia · 2024Article
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Authors and funding
3 authors.
Funding
Abstract
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 under-explored. 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 and generated 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, which labels sentences from a public EHR collection with AD-related signs and symptoms; and (3) a bronze dataset created by the label-to-data method 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. We find that 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.
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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.