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References from Relapse prediction and individualized treatment-effect modeling in relapsing multiple sclerosis: a systematic review. Local targets link to admitted publications; unresolved targets remain external evidence.
Decoding disease burden in multiple sclerosis: the role of IL-10 as biomarker of neuroinflammation and neurodegeneration
10.1016/j.msard.2025.106540 · 2025 · External reference
Advancements in multiple sclerosis
10.1111/imj.70023 · 2025 · External reference
Comparative effectiveness of disease-modifying therapies for highly active relapsing-remitting multiple sclerosis despite previous treatment - a systematic review and network meta-analysis
10.1186/s12883-025-04338-7 · 2025 · External reference
Multiple sclerosis relapse incomplete recovery and associated factors – a systematic review and meta-analysis
10.1016/j.msard.2025.106507 · 2025 · External reference
Clinical and psychological factors associated with fear of relapse in people with multiple sclerosis: a cross-sectional study
10.1016/j.jocn.2025.111210 · 2025 · External reference
Identification and management of subclinical disease activity in early multiple sclerosis: a review
10.1007/s00415-023-12021-5 · 2024 · External reference
Directions for advancing prognostic assessments in multiple sclerosis: qualitative Insights from MS specialist Interviews
10.1016/j.jns.2026.125756 · 2026 · External reference
Harnessing artificial intelligence for the diagnosis, treatment and research of multiple sclerosis
10.3390/sclerosis3020015 · 2025 · External reference
AI-powered disease progression prediction in multiple sclerosis using magnetic resonance imaging: a systematic review and meta-analysis
10.1080/01616412.2025.2532038 · 2026 · External reference
Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis
10.1002/14651858.cd013606.pub2 · 2023 · External reference
The PRISMA 2020 statement: an updated guideline for reporting systematic reviews
10.1136/bmj.n71 · 2021 · External reference
Leveraging electronic health records data to predict multiple sclerosis disease activity
10.1002/acn3.51324 · 2021 · External reference
Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis
10.1186/s41512-021-00106-6 · 2021 · External reference
Explainable Artificial Intelligence to predict clinical outcomes in type 1 diabetes and relapsing-remitting multiple sclerosis adult patients
10.1016/j.imu.2023.101349 · 2023 · External reference
Supervised Machine Learning Models for Multiple Sclerosis Relapse Probability Prognosis using Clinical Data
10.1109/icwr57742.2023.10139225 · 2023 · External reference
Comparing traditional modeling approaches versus predictive analytics methods for predicting multiple sclerosis relapse
10.1016/j.msard.2021.103330 · 2022 · External reference
Two-stage prediction model for heterogeneous effects of treatments
10.1002/sim.9034 · 2021 · External reference
Multivariable prognostic prediction of efficacy and safety outcomes and response to fingolimod in people with relapsing-remitting multiple sclerosis
10.1016/j.msard.2024.106247 · 2025 · External reference
Overall and patient-specific comparative effectiveness of dimethyl fumarate versus teriflunomide: a novel approach to precision medicine applied to the German NeuroTrans Data Multiple Sclerosis Registry
10.1177/20552173231194353 · 2023 · External reference
Framework for personalized prediction of treatment response in relapsing remitting multiple sclerosis
10.1186/s12874-020-0906-6 · 2020 · External reference
Computational classifiers for predicting the short-term course of Multiple sclerosis
10.1186/1471-2377-11-67 · 2011 · External reference
Plasma neurofilament light chain concentrations as a biomarker of clinical and radiologic outcomes in relapsing multiple sclerosis: Post hoc analysis of Phase 3 ozanimod trials
10.1111/ene.15009 · 2021 · External reference
Unresolved reference
External reference
Machine Learning Models Highlight the Impact of Pollution and Weather Patterns on Relapse Occurrence in Multiple Sclerosis Patients
10.1109/bibm62325.2024.10822214 · 2024 · External reference
Deep learning-based PET/MR radiomics for the classification of annualized relapse rate in multiple sclerosis
10.1016/j.msard.2023.104750 · 2023 · External reference
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024 · External reference
Predicting multiple sclerosis prognosis using AI and machine learning: integrating clinical, immunological, and radiological variables
10.3389/fneur.2025.1712953 · 2026 · External reference
Deep learning to predict progression independent of relapse activity at a first demyelinating event
10.1093/braincomms/fcaf243 · 2025 · External reference
A guide to systematic review and meta-analysis of prediction model performance
10.1136/bmj.i6460 · 2017 · External reference
Evaluation of clinical prediction models (part 3): calculating the sample size required for an external validation study
10.1136/bmj-2023-074821 · 2024 · External reference
Calibration: the Achilles heel of predictive analytics
10.1186/s12916-019-1466-7 · 2019 · External reference
“Overfitting, Underfitting and General Model Overconfidence and Under-Performance Pitfalls and Best Practices in Machine Learning and AI
10.1007/978-3-031-39355-6_10 · 2024 · External reference
Generalization—a key challenge for responsible AI in patient-facing clinical applications
10.1038/s41746-024-01127-3 · 2024 · External reference
Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital
10.1371/journal.pone.0298328 · 2024 · External reference
Estimation of minimal data sets sizes for machine learning predictions in digital mental health interventions
10.1038/s41746-024-01360-w · 2024 · External reference
MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies
10.1016/j.nicl.2022.102972 · 2022 · External reference
Predictive MRI biomarkers in MS—a critical review
10.3390/medicina58030377 · 2022 · External reference
Initiation of first disease-modifying treatment for multiple sclerosis patients in the Czech republic from 2013 to 2016: data from the national registry ReMuS
10.1016/j.msard.2019.08.003 · 2019 · External reference
The Czech National MS Registry (ReMuS): Data trends in multiple sclerosis patients whose first disease-modifying therapies were initiated from 2013 to 2021
10.5507/bp.2023.015 · 2024 · External reference
Introducing a core dataset for real-world data in multiple sclerosis registries and cohorts: Recommendations from a global task force
10.1177/13524585231216004 · 2024 · External reference
Big multiple sclerosis data network: novel modelling approaches for real-world data analysis
10.1007/s00415-025-13439-9 · 2025 · External reference
AI-driven reclassification of multiple sclerosis progression
10.1038/s41591-025-03901-6 · 2025 · External reference
Developing clinical prediction models: a step-by-step guide
10.1136/bmj-2023-078276 · 2024 · External reference
Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making
10.1136/bmjhci-2023-100784 · 2023 · External reference
FUTURE-AI: international consensus guideline for trustworthy and deployable artificial intelligence in healthcare
10.1136/bmj-2024-081554 · 2025 · External reference
Why do probabilistic clinical models fail to transport between sites
10.1038/s41746-024-01037-4 · 2024 · External reference
Protocol for development of a reporting guideline (TRIPOD-AI) and risk of bias tool (PROBAST-AI) for diagnostic and prognostic prediction model studies based on artificial intelligence
10.1136/bmjopen-2020-048008 · 2021 · External reference
Predicting disease severity in multiple sclerosis using multimodal data and machine learning
10.1007/s00415-023-12132-z · 2024 · External reference
Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review
10.1007/s00415-024-12651-3 · 2024 · External reference