A comparative analysis of large language models versus traditional information extraction methods for real-world evidence of patient symptomatology in acute and post-acute sequelae of SARS-CoV-2.

A comparative analysis of large language models versus traditional information extraction methods for real-world evidence of patient symptomatology in acute and post-acute sequelae of SARS-CoV-2.

<h4>Background</h4>Patient symptoms, crucial for disease progression and diagnosis, are often captured in unstructured clinical notes. Large language models (LLMs) offer potential advantages in extracting patient symptoms compared to traditional rule-based information extraction (IE) sys...

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Main Authors: Vedansh Thakkar, Greg M Silverman, Abhinab Kc, Nicholas E Ingraham, Emma K Jones, Samantha King, Genevieve B Melton, Rui Zhang, Christopher J Tignanelli
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2025-01-01
Series:PLoS ONE
Online Access:https://doi.org/10.1371/journal.pone.0323535
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https://doi.org/10.1371/journal.pone.0323535

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