Optimizing Clinical Temporal Relation Extraction With Large Language Models: Comparative Analysis.

Jianping He¹, Zenan Sun¹, Laila Rasmy¹, Haifang Li², Jianfu Li², Evan Yu¹, Degui Zhi¹, Cui Tao²

¹McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States.

²Department of Artificial Intelligence and Informatics, Mayo Clinic in Florida, 4500 San Pablo Rd S, Jacksonville, FL, 32224, United States, 1 904 956 3256.

PubMed DOI

Abstract

BACKGROUND: Clinical temporal relation extraction (CTRE) is essential for reconstructing patient timelines from unstructured electronic health records (EHRs). However, the linguistic complexity of clinical notes and the high cost of expert annotation impede the development of large-scale training corpora. While large language models (LLMs) have transformed general natural language processing, thei…

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