ArticleScientific reports2020
Prediction of disease progression and outcomes in multiple sclerosis with machine learning.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Association of Vitamin D Polygenic Risk Scores and Disease Outcome in People With Multiple Sclerosis.Neurology(R) neuroimmunology & neuroinflammation · 2023Pooled it
- Synthetic control methods for n-of-1 and parallel-group trials in Alzheimer's disease: A proof-of-concept study using the I-CONECT.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Trial
- Knockoff-Based Fine Mapping of MS-Associated SNPs in Sardinian Trios.Biochemical genetics · 2026Article
- Mechanistic Insights into the Role of Artificial Intelligence and Machine Learning in the Diagnosis and Management of Multiple Sclerosis.Pathophysiology : the official journal of the International Society for Pathophysiology · 2026Review
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- FHIR-Former: enhancing clinical predictions through Fast Healthcare Interoperability Resources and large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Machine Learning Analysis Applied to Prediction of Early Progression Independent of Relapse Activity in Multiple Sclerosis Patients.European journal of neurology · 2025Article
- MSLesSeg: baseline and benchmarking of a new Multiple Sclerosis Lesion Segmentation dataset.Scientific data · 2025Article
- Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis.Scientific reports · 2025Observational
- Prediction of white matter hyperintensities evolution one-year post-stroke from a single-point brain MRI and stroke lesions information.Scientific reports · 2025Article
- Machine learning for refining interpretation of magnetic resonance imaging scans in the management of multiple sclerosis: a narrative review.Royal Society open science · 2025Review
- Current challenges in secondary progressive multiple sclerosis: diagnosis, activity detection and treatment.Frontiers in immunology · 2025Review
- Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development.Clinical and translational science · 2024Review
- Long-Term Disability Outcomes in Relapsing-Remitting Multiple Sclerosis Patients: Impact of Clinical and Demographic Factors on Disease Progression.Journal of clinical medicine · 2024Article
- Discovering genotype-phenotype relationships with machine learning and the Visual Physiology Opsin Database (VPOD).GigaScience · 2024Article
- Initial Evaluation of lncRNA A2M-AS1 Gene Expression in Multiple Sclerosis Patients.Advanced biomedical research · 2024Article
- Cross-population applications of genomics to understand the risk of multifactorial traits involving inflammation and immunity.Cambridge prisms. Precision medicine · 2024Review
- Explainable machine learning on baseline MRI predicts multiple sclerosis trajectory descriptors.PloS one · 2024Article
- Predicting multiple sclerosis severity with multimodal deep neural networks.BMC medical informatics and decision making · 2023Article
- Imbalanced class distribution and performance evaluation metrics: A systematic review of prediction accuracy for determining model performance in healthcare systems.PLOS digital health · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiple Sclerosis is a chronic inflammatory disease, affecting the Central Nervous System and leading to irreversible neurological damage, such as long term functional impairment and disability. It has no cure and the symptoms vary widely, depending on the affected regions, amount of damage, and the ability to activate compensatory mechanisms, which constitutes a challenge to evaluate and predict its course. Additionally, relapsing-remitting patients can evolve its course into a secondary progressive, characterized by a slow progression of disability independent of relapses. With clinical information from Multiple Sclerosis patients, we developed a machine learning exploration framework concerning this disease evolution, more specifically to obtain three predictions: one on conversion to secondary progressive course and two on disease severity with rapid accumulation of disability, concerning the 6th and 10th years of progression. For the first case, the best results were obtained within two years: AUC=[Formula: see text], sensitivity=[Formula: see text] and specificity=[Formula: see text]; and for the second, the best results were obtained for the 6th year of progression, also within two years: AUC=[Formula: see text], sensitivity=[Formula: see text], and specificity=[Formula: see text]. The Expanded Disability Status Scale value, the majority of functional systems, affected functions during relapses, and age at onset were described as the most predictive features. These results demonstrate the possibility of predicting Multiple Sclerosis progression by using machine learning, which may help to understand this disease's dynamics and thus, advise physicians on medication intake.
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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.