ArticleAnnals of clinical and translational neurology2021
Leveraging electronic health records data to predict multiple sclerosis disease activity.
Article in Annals of clinical and translational neurology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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.
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, 2 syntheses or guidelines pooled it.
- Relapse prediction and individualized treatment-effect modeling in relapsing multiple sclerosis: a systematic review.Frontiers in public health · 2026Pooled it
- Natural language processing in clinical neuroscience and psychiatry: A review.Frontiers in psychiatry · 2022Pooled it
- Are there missed warning signals before traffic-related traumatic spinal cord injury? An exploratory case-control study using electronic health records.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2026Article
- Knowledge graph-guided multiple sclerosis identification and therapeutic trend analysis: Real-world evidence from two large healthcare systems.PLOS digital health · 2026Article
- Identifying Patients at High Risk for Colorectal Carcinoma Using the Electronic Health Record.Digestive diseases and sciences · 2026Article
- Integration of fairness-awareness into clinical language processing models.Communications medicine · 2026Article
- Phenotyping Healthcare Use 2-3 Decades Before the First Multiple Sclerosis Demyelinating Event.Annals of clinical and translational neurology · 2025Article
- Assessing the capture of sociodemographic information in electronic medical records to inform clinical decision making.PloS one · 2025Article
- Clinical and genetic contributions to medical comorbidity in bipolar disorder: a study using electronic health records-linked biobank data.Molecular psychiatry · 2024Article
- Association between serum multi-protein biomarker profile and real-world disability in multiple sclerosis.Brain communications · 2024Article
- Clustering Sequence Data with Mixture Markov Chains with Covariates Using Multiple Simplex Constrained Optimization Routine (MSiCOR).Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2024Article
- Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis.The Cochrane database of systematic reviews · 2023Review
- Article
- Toward attention-based learning to predict the risk of brain degeneration with multimodal medical data.Frontiers in neuroscience · 2022Article
- Temporal trends of multiple sclerosis disease activity: Electronic health records indicators.Multiple sclerosis and related disorders · 2022Article
- Comparison of Dimethyl Fumarate vs Fingolimod and Rituximab vs Natalizumab for Treatment of Multiple Sclerosis.JAMA network open · 2021Article
- The Application of Projection Word Embeddings on Medical Records Scoring System.Healthcare (Basel, Switzerland) · 2021Article
- Multiple sclerosis relapse risk factors across treatment eras.Multiple sclerosis journal - experimental, translational and clinicalArticle
- Validation of a machine learning approach to estimate expanded disability status scale scores for multiple sclerosis.Multiple sclerosis journal - experimental, translational and clinicalArticle
- Clinical and Research Applications of the Electronic Medical Record in Multiple Sclerosis: A Narrative Review of Current Uses and Future Applications.International journal of MS careArticle
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
objectiveNo relapse risk prediction tool is currently available to guide treatment selection for multiple sclerosis (MS). Leveraging electronic health record (EHR) data readily available at the point of care, we developed a clinical tool for predicting MS relapse risk.
methodsUsing data from a clinic-based research registry and linked EHR system between 2006 and 2016, we developed models predicting relapse events from the registry in a training set (n = 1435) and tested the model performance in an independent validation set of MS patients (n = 186). This iterative process identified prior 1-year relapse history as a key predictor of future relapse but ascertaining relapse history through the labor-intensive chart review is impractical. We pursued two-stage algorithm development: (1) L
resultsThe final model, comprising age, disease duration, and imputed prior 1-year relapse history, achieved a predictive AUC and F score of 0.707 and 0.307, respectively. The performance was significantly better than the baseline model (age, sex, race/ethnicity, and disease duration) and noninferior to a model containing actual prior 1-year relapse history. The predicted risk probability declined with disease duration and age.
conclusionOur novel machine-learning algorithm predicts 1-year MS relapse with accuracy comparable to other clinical prediction tools and has applicability at the point of care. This EHR-based two-stage approach of outcome prediction may have application to neurological disease beyond MS.
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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.