Development of a Natural Language Processing Tool to Extract Acupuncture Point Location Terms

Li, Yiming; Peng, Xueqing; Li, Jianfu; Peng, Suyuan; Pei, Donghong; Tao, Cui; Xu, Hua; Hong, Na

doi:10.1109/ichi57859.2023.00053

Cited by 5 publications

(1 citation statement)

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“…It is evident that the model exhibits a predominant challenge in dealing with boundary mismatch, false positives and false negatives, which can be attributed to several factors. The quality and representativeness of the training data play a significant role; inconsistent or limited annotations can lead to mismatches and incorrect identifications [ 60 , 61 ]. The inherent complexity of distinguishing similar and potentially overlapping entities adds to the challenge.…”

Section: Discussionmentioning

confidence: 99%

AE-GPT: Using Large Language Models to extract adverse events from surveillance reports-A use case with influenza vaccine adverse events

Li,

et al. 2024

PLoS ONE

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Though Vaccines are instrumental in global health, mitigating infectious diseases and pandemic outbreaks, they can occasionally lead to adverse events (AEs). Recently, Large Language Models (LLMs) have shown promise in effectively identifying and cataloging AEs within clinical reports. Utilizing data from the Vaccine Adverse Event Reporting System (VAERS) from 1990 to 2016, this study particularly focuses on AEs to evaluate LLMs’ capability for AE extraction. A variety of prevalent LLMs, including GPT-2, GPT-3 variants, GPT-4, and Llama2, were evaluated using Influenza vaccine as a use case. The fine-tuned GPT 3.5 model (AE-GPT) stood out with a 0.704 averaged micro F1 score for strict match and 0.816 for relaxed match. The encouraging performance of the AE-GPT underscores LLMs’ potential in processing medical data, indicating a significant stride towards advanced AE detection, thus presumably generalizable to other AE extraction tasks.

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Section: Discussionmentioning

confidence: 99%