Large Language Models Outperform Traditional Natural Language Processing Methods in Extracting Patient-Reported Outcomes in Inflammatory Bowel Disease

Large Language Models Outperform Traditional Natural Language Processing Methods in Extracting Patient-Reported Outcomes in Inflammatory Bowel Disease

Background and Aims: Patient-reported outcomes (PROs) are vital in assessing disease activity and treatment outcomes in inflammatory bowel disease (IBD). However, manual extraction of these PROs from the free-text of clinical notes is burdensome. We aimed to improve data curation from free-text info...

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Main Authors: Perseus V. Patel, Conner Davis, Amariel Ralbovsky, Daniel Tinoco, Christopher Y.K. Williams, Shadera Slatter, Behzad Naderalvojoud, Michael J. Rosen, Tina Hernandez-Boussard, Vivek Rudrapatna
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Gastro Hep Advances
Subjects:
Machine learning
GPT-4
PaLM-2
Clinical data science
Online Access:http://www.sciencedirect.com/science/article/pii/S2772572324001584
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http://www.sciencedirect.com/science/article/pii/S2772572324001584

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