ArticleMultiple sclerosis journal - experimental, translational and clinical
Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity.
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 5 papers.
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Who cites it
5 citing papers in PubMed.
- A quantitative multi-parameter mapping protocol standardized for clinical research in multiple sclerosis.Scientific reports · 2024Article
- Non-invasive Assessment of Cerebral Hemodynamics Using Resting-State Functional Magnetic Resonance Imaging in Multiple Sclerosis and Age-Related White Matter Lesions.Human brain mapping · 2024Article
- Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.Journal of neurology · 2024Review
- Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis.Journal of neurology · 2024Article
- A review on multiple sclerosis prognostic findings from imaging, inflammation, and mental health studies.Frontiers in human neuroscience · 2023Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Lack of easy-to-interpret disease activity prediction methods in early MS can lead to worse patient prognosis. Objectives: Using machine learning (multiple kernel learning - MKL) models, we assessed the prognostic value of various clinical and MRI measures for disease activity. Methods: Early MS patients ( Results: A total of 681 visits were included, where 81 (55%) patients had high disease activity in a combined end point measure using all follow-up visits. MKL model discrimination performance was moderate (AUC ≥ 0.62); however, modelling with combined clinical and cropped LV kernels gave the highest prediction performance (AUC = 0.70). Conclusions: MRIs contain valuable information on future disease activity, especially in and around the LV. MKL techniques for combining different data types can be used for the prediction of disease activity in a relatively small MS cohort.
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