ArticleMultiple sclerosis journal - experimental, translational and clinical
Validation of a machine learning approach to estimate expanded disability status scale scores for multiple sclerosis.
Article in Multiple sclerosis journal - experimental, translational and clinical. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Contribution of artificial intelligence research to the development of inclusive applications for people with disabilities: A scoping review (2015-2025).PLOS digital health · 2026Article
- Serum Vitamin E Levels in Multiple Sclerosis: Association with Diagnosis, Cognitive Function, Disability, and Depressive Symptoms.Molecular neurobiology · 2026Article
- Neural networks to estimate multiple sclerosis disability and predict progression using routinely collected healthcare data.Multiple sclerosis (Houndmills, Basingstoke, England) · 2025Article
- Detecting New Lesions Using a Large Language Model: Applications in Real-World Multiple Sclerosis Datasets.Annals of neurology · 2025Article
- Integrating large language models in care, research, and education in multiple sclerosis management.Multiple sclerosis (Houndmills, Basingstoke, England) · 2024Review
- Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review.JAMIA open · 2024Review
- Utilizing Aerobic Capacity Data for EDSS Score Estimation in Multiple Sclerosis: A Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2024Article
- Claims-based algorithm to estimate the Expanded Disability Status Scale for multiple sclerosis in a German health insurance fund: a validation study using patient medical records.Frontiers in neurology · 2023Article
- Machine learning to deal with missing disability status: Ascertainment and imputation of outcomes should be distinguished.Multiple sclerosis journal - experimental, translational and clinicalArticle
- Validated, Quantitative, Machine Learning-Generated Neurologic Assessment of Multiple Sclerosis Using a Mobile Application.International journal of MS careArticle
- Reply to Letter to the Editor: Machine learning to deal with missing disability status.Multiple sclerosis journal - experimental, translational and clinicalArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Disability assessment using the Expanded Disability Status Scale (EDSS) is important to inform treatment decisions and monitor the progression of multiple sclerosis. Yet, EDSS scores are documented infrequently in electronic medical records. Objective: To validate a machine learning model to estimate EDSS scores for multiple sclerosis patients using clinical notes from neurologists. Methods: A machine learning model was developed to estimate EDSS scores on specific encounter dates using clinical notes from neurologist visits. The OM1 MS Registry data were used to create a training cohort of 2632 encounters and a separate validation cohort of 857 encounters, all with clinician-recorded EDSS scores. Model performance was assessed using the area under the receiver-operating-characteristic curve (AUC), positive predictive value (PPV), and negative predictive value (NPV), calculated using a binarized version of the outcome. The Spearman Results: The model had a PPV of 0.85, NPV of 0.85, and AUC of 0.91. The model had a Spearman Conclusion: EDSS scores can be estimated with very good performance using a machine learning model applied to clinical notes, thus increasing the utility of real-world data sources for research purposes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.