ArticleNPJ digital medicine2025
Conformal prediction enables disease course prediction and allows individualized diagnostic uncertainty in multiple sclerosis.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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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
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Who cites it
9 citing papers in PubMed.
- Reliable Uncertainty Under Class Imbalance and Distribution Shift: Class-Conditional Conformal Prediction of Multiple Sclerosis.medRxiv : the preprint server for health sciences · 2026Article
- In Silico Psycho-Oncology: Understanding Resilience Pathways in Breast Cancer-Determinants of Longitudinal Depression and Quality-of-Life Trajectories.Journal of personalized medicine · 2026Article
- Defining operational safety in clinical artificial intelligence systems.NPJ digital medicine · 2026Article
- Targeted CSF metabolomics and conformal prediction improve diagnostic accuracy of normal pressure hydrocephalus.Fluids and barriers of the CNS · 2026Article
- Integrating conformal prediction with machine learning for uncertainty-aware risk stratification in coronary artery disease.Frontiers in cardiovascular medicine · 2026Article
- Knowledgeable Language Models as Black-Box Optimizers for Personalized Medicine.... International Conference on Learning Representations · 2026Article
- From lesion detection to outcome prediction: artificial intelligence and deep learning applications in multiple sclerosis.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2025Review
- Error Reduction in Leukemia Machine Learning Classification With Conformal Prediction.JCO clinical cancer informatics · 2025Article
- Enhancing clinical reliability in pressure injury prediction: A conformal prediction approach with machine learning models.Digital healthArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Accurate assessment of progression and disease course in multiple sclerosis (MS) is vital for timely and appropriate clinical intervention. The gradual transition from relapsing-remitting MS (RRMS) to secondary progressive MS (SPMS) is often diagnosed retrospectively with a typical delay of three years. To address this diagnostic delay, we developed a predictive model that uses electronic health records to distinguish between RRMS and SPMS at each individual visit. To enable reliable predictions, conformal prediction was implemented at the individual patient level with a confidence of 93%. Our model accurately predicted the change in diagnosis from RRMS to SPMS for patients who transitioned during the study period. Additionally, we identified new patients who, with high probability, are in the transition phase but have not yet received a clinical diagnosis. Our methodology aids in monitoring MS progression and proactively identifying transitioning patients. An anonymized model is available at https://msp-tracker.serve.scilifelab.se/ .
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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.