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: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0323535 |
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