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Aprovechamiento de Modelos de Lenguaje Grandes para Derivar Evaluaciones de Progresión de Esclerosis Múltiple a

Sy Hwang¹, Sunil Thomas², Heather Williams²

¹Institute for Biomedical Informatics, Perelman School of Medicine University of Pennsylvania, Philadelphia, PA, USA, sy.hwang@pennmedicine.upenn.edu.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|February 27, 2026

Abstract:

Ascertainment of multiple sclerosis (MS) progression is important for informing clinical care decisions and supporting biomedical research. However, the details to infer a patient's MS progression status are locked within clinical notes. In this feasibility study, we assessed the feasibility of developing and validating a large language model (LLM)-based EDSS and FS classifier for ascertaining MS progression from clinical notes.

Palabras clave:

Esclerosis Múltiple Modelos de Lenguaje Grandes Notas Clínicas Progresión de la Enfermedad Evaluación Clínica Inteligencia Artificial Procesamiento del Lenguaje Natural EDSS Puntuaciones de Sistemas Funcionales

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