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Related Experiment Video

Updated: May 5, 2026

The Multiple Sclerosis Performance Test MSPT: An iPad-Based Disability Assessment Tool

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Published on: June 30, 2014

Leveraging Large Language Models to Derive Multiple Sclerosis Progression Assessments from Clinical Notes: 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

Summary

This summary is machine-generated.

This study explored using large language models (LLMs) to analyze clinical notes for multiple sclerosis (MS) progression. The goal was to develop a feasible classifier for EDSS and FS scores from patient records.

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Area of Science:

Neurology
Artificial Intelligence
Clinical Informatics

Background:

Accurate assessment of multiple sclerosis (MS) progression is crucial for patient care and research.
Key progression indicators are often embedded within unstructured clinical notes.
Current methods for extracting this data can be labor-intensive.

Purpose of the Study:

To evaluate the feasibility of developing and validating a large language model (LLM)-based classifier.
To ascertain multiple sclerosis (MS) progression using clinical notes.
To extract Expanded Disability Status Scale (EDSS) and Functional Systems (FS) scores automatically.

Main Methods:

Development of a large language model (LLM) classifier.
Utilizing clinical notes as the data source.
Validation of the LLM's performance in classifying MS progression indicators.

Main Results:

The study assessed the feasibility of the LLM-based approach.
Preliminary findings indicate potential for automated MS progression ascertainment.
Further validation is required to confirm accuracy and reliability.

Conclusions:

Developing an LLM-based classifier is a feasible approach for MS progression ascertainment from clinical notes.
This method holds promise for improving efficiency in clinical care and research.
Future work should focus on robust validation and integration into clinical workflows.