Large Language Models for Granularized Barrett's Esophagus Diagnosis Classification

• URL: http://arxiv.org/abs/2308.08660v1
• Date: Wed, 16 Aug 2023 20:17:46 GMT
• Title: Large Language Models for Granularized Barrett's Esophagus Diagnosis Classification
• Authors: Jenna Kefeli, Ali Soroush, Courtney J. Diamond, Haley M. Zylberberg, Benjamin May, Julian A. Abrams, Chunhua Weng, Nicholas Tatonetti
• Abstract summary: Laborious manual chart review is required to extract key diagnostic phenotypes from pathology reports. We developed a generalizable transformer-based method to automate data extraction. Binary dysplasia extraction achieves 0.964 F1-score, while the multi-class model achieves 0.911 F1-score.
• Score: 4.144525746262123
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Diagnostic codes for Barrett's esophagus (BE), a precursor to esophageal cancer, lack granularity and precision for many research or clinical use cases. Laborious manual chart review is required to extract key diagnostic phenotypes from BE pathology reports. We developed a generalizable transformer-based method to automate data extraction. Using pathology reports from Columbia University Irving Medical Center with gastroenterologist-annotated targets, we performed binary dysplasia classification as well as granularized multi-class BE-related diagnosis classification. We utilized two clinically pre-trained large language models, with best model performance comparable to a highly tailored rule-based system developed using the same data. Binary dysplasia extraction achieves 0.964 F1-score, while the multi-class model achieves 0.911 F1-score. Our method is generalizable and faster to implement as compared to a tailored rule-based approach.

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