臨床ノートから多発性硬化症の進行を導き出すための大規模言語モデルの活用：実現可能性スタディ

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.

キーワード:

大規模言語モデル多発性硬化症臨床ノート疾患進行 EDSSスコア FSスコア自然言語処理医療情報学実現可能性スタディ自動化

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