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        <title>Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data</title>
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        <namePart type="given">Rumeng</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data
%A Li, Rumeng
%A Wang, Xun
%A Yu, Hong
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F li-etal-2023-two
%X 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.
%R 10.18653/v1/2023.findings-emnlp.474
%U https://aclanthology.org/2023.findings-emnlp.474
%U https://doi.org/10.18653/v1/2023.findings-emnlp.474
%P 7129-7143

Markdown (Informal)

[Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data](https://aclanthology.org/2023.findings-emnlp.474) (Li et al., Findings 2023)

• Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data (Li et al., Findings 2023)

ACL

• Rumeng Li, Xun Wang, and Hong Yu. 2023. Two Directions for Clinical Data Generation with Large Language Models: Data-to-Label and Label-to-Data. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7129–7143, Singapore. Association for Computational Linguistics.