ArticleNPJ digital medicine2020
Ensemble learning predicts multiple sclerosis disease course in the SUMMIT study.
Article in NPJ digital medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 29 papers.
What it found
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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
29 citing papers in PubMed, 65 citations in OpenAlex.
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- An interpretable machine learning model for diabetic foot risk classification in patients with diabetes.Scientific reports · 2026Article
- An intelligent auxiliary diagnostic system for early osteoporosis screening using stacking ensemble learning.BMC medical informatics and decision making · 2026Article
- Research on a Predictive Model for Microalbuminuria in Type 2 Diabetes Based on Machine Learning and SHAP Analysis.International journal of endocrinology · 2026Article
- Personalized and real time hemodynamic management in critical care using Dynamic Cohort Ensemble Learning (DynaCEL).NPJ digital medicine · 2025Article
- The role of AI for MRI-analysis in multiple sclerosis-A brief overview.Frontiers in artificial intelligence · 2025Review
- Predicting multiple sclerosis disease progression and outcomes with machine learning and MRI-based biomarkers: a review.Journal of neurology · 2024Review
- Current and future role of MRI in the diagnosis and prognosis of multiple sclerosis.The Lancet regional health. Europe · 2024Review
- Evaluation of machine learning-based classification of clinical impairment and prediction of clinical worsening in multiple sclerosis.Journal of neurology · 2024Article
- Gene-Based Predictive Modelling for Enhanced Detection of Systemic Lupus Erythematosus Using CNN-Based DL Algorithm.Diagnostics (Basel, Switzerland) · 2024Article
- Gut microbiome features and metabolites in non-alcoholic fatty liver disease among community-dwelling middle-aged and older adults.BMC medicine · 2024Article
- Predicting disease severity in multiple sclerosis using multimodal data and machine learning.Journal of neurology · 2024Article
- Explainable machine learning on baseline MRI predicts multiple sclerosis trajectory descriptors.PloS one · 2024Article
- The State of Machine Learning in Outcomes Prediction of Transsphenoidal Surgery: A Systematic Review.Journal of neurological surgery. Part B, Skull base · 2023Article
- Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.The Cochrane database of systematic reviews · 2023Review
- Computed tomography angiography-based radiomics model for predicting carotid atherosclerotic plaque vulnerability.Frontiers in neurology · 2023Article
- Accounting for uncertainty in training data to improve machine learning performance in predicting new disease activity in early multiple sclerosis.Frontiers in neurology · 2023Article
- Exploration of machine learning methods to predict systemic lupus erythematosus hospitalizations.Lupus · 2022Article
- Comparing two machine learning approaches in predicting lupus hospitalization using longitudinal data.Scientific reports · 2022Article
- An Enhanced Multiple Sclerosis Disease Diagnosis via an Ensemble Approach.Diagnostics (Basel, Switzerland) · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
12 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rate of disability accumulation varies across multiple sclerosis (MS) patients. Machine learning techniques may offer more powerful means to predict disease course in MS patients. In our study, 724 patients from the Comprehensive Longitudinal Investigation in MS at Brigham and Women's Hospital (CLIMB study) and 400 patients from the EPIC dataset, University of California, San Francisco, were included in the analysis. The primary outcome was an increase in
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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.