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

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