ReviewLife (Basel, Switzerland)2021
Machine Learning Use for Prognostic Purposes in Multiple Sclerosis.
Review in Life (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
20 citing papers in PubMed, 52 citations in OpenAlex.
- Strategies for integrating artificial intelligence and cognitive assessment to predict disability progression in relapsing-remitting multiple sclerosis: A model development study.PLOS digital health · 2026Article
- Combining magnetic resonance imaging and evoked potentials enhances machine learning prediction of multiple sclerosis disability worsening.Frontiers in immunology · 2026Article
- Artificial Intelligence-Driven Neuromodulation in Neurodegenerative Disease: Precision in Chaos, Learning in Loss.Biomedicines · 2025Review
- Refining Prognostic Factors in Adult-Onset Multiple Sclerosis: A Narrative Review of Current Insights.International journal of molecular sciences · 2025Review
- Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data.NPJ digital medicine · 2025Article
- The role of AI for MRI-analysis in multiple sclerosis-A brief overview.Frontiers in artificial intelligence · 2025Review
- Time-Dependent Deep Learning Prediction of Multiple Sclerosis Disability.Journal of imaging informatics in medicine · 2024Article
- Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.Journal of neurology · 2024Review
- Can machine learning predict late seizures after intracerebral hemorrhages? Evidence from real-world data.Epilepsy & behavior : E&B · 2024Article
- Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study.PLOS digital health · 2024Article
- Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.The Cochrane database of systematic reviews · 2023Review
- Assessing treatment switch among patients with multiple sclerosis: A machine learning approach.Exploratory research in clinical and social pharmacy · 2023Article
- Diffusion tensor imaging metrics associated with future disability in multiple sclerosis.Scientific reports · 2023Article
- Retrospective cohort study to devise a treatment decision score predicting adverse 24-month radiological activity in early multiple sclerosis.Therapeutic advances in neurological disorders · 2023Article
- Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities.Sensors (Basel, Switzerland) · 2022Review
- Comparison of Machine Learning Methods Using Spectralis OCT for Diagnosis and Disability Progression Prognosis in Multiple Sclerosis.Annals of biomedical engineering · 2022Article
- Towards Multimodal Machine Learning Prediction of Individual Cognitive Evolution in Multiple Sclerosis.Journal of personalized medicine · 2021Review
- Prediction of high and low disease activity in early MS patients using multiple kernel learning identifies importance of lateral ventricle intensity.Multiple sclerosis journal - experimental, translational and clinicalArticle
- Deep Learning and The Retina: A New Frontier in Multiple Sclerosis Diagnosis.Current health sciences journalReview
- Homocysteine as a Predictor Tool in Multiple Sclerosis.Discoveries (Craiova, Romania)Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The course of multiple sclerosis begins with a relapsing-remitting phase, which evolves into a secondarily progressive form over an extremely variable period, depending on many factors, each with a subtle influence. To date, no prognostic factors or risk score have been validated to predict disease course in single individuals. This is increasingly frustrating, since several treatments can prevent relapses and slow progression, even for a long time, although the possible adverse effects are relevant, in particular for the more effective drugs. An early prediction of disease course would allow differentiation of the treatment based on the expected aggressiveness of the disease, reserving high-impact therapies for patients at greater risk. To increase prognostic capacity, approaches based on machine learning (ML) algorithms are being attempted, given the failure of other approaches. Here we review recent studies that have used clinical data, alone or with other types of data, to derive prognostic models. Several algorithms that have been used and compared are described. Although no study has proposed a clinically usable model, knowledge is building up and in the future strong tools are likely to emerge.
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What OpenQuestion holds
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.