| Autor: |
Kartsonis, William, Pastena, Paola, Hirsch, Kelly, Gilotra, Kevin, Murundi, Shamanth, Raiker, Ashna, de la Bastide, Chris, Martinez, Camilo, Tassiopoulos, Apostolos |
| Zdroj: |
Annals of Vascular Surgery; 20240101, Issue: Preprints |
| Abstrakt: |
Incidental findings of aortic aneurysms (AAs) often go unreported, and established patients are frequently lost to follow-up. Natural language processing (NLP) offers a promising solution to address these issues. While rule-based NLP methods have shown some success, recent advancements in transformer-based large language models (LLMs) remain underutilized. This study has three aims: (1) to evaluate the effectiveness of our innovative transformer-based NLP pipeline regarding AA detection; (2) to detail the clinical impact by quantifying the number of patients who could benefit from such technology; and (3) to use this information to help coordinate appointments with patients, ensuring proper monitoring and management. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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