Two Directions for Clinical Data Generation with Large Language Models:
  Data-to-Label and Label-to-Data
Rumeng Li Xun Wang Hong Yu
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.