Predicting disease severity in multiple sclerosis using multimodal data and machine learning
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- dc.contributor.author Andorrà, Magí
- dc.contributor.author Freire, Ana
- dc.contributor.author Baeza Yates, Ricardo
- dc.contributor.author Villoslada, Pablo
- dc.date.accessioned 2024-10-25T06:09:48Z
- dc.date.available 2024-10-25T06:09:48Z
- dc.date.issued 2024
- dc.description.abstract Background: Multiple sclerosis patients would benefit from machine learning algorithms that integrates clinical, imaging and multimodal biomarkers to define the risk of disease activity. Methods: We have analysed a prospective multi-centric cohort of 322 MS patients and 98 healthy controls from four MS centres, collecting disability scales at baseline and 2 years later. Imaging data included brain MRI and optical coherence tomography, and omics included genotyping, cytomics and phosphoproteomic data from peripheral blood mononuclear cells. Predictors of clinical outcomes were searched using Random Forest algorithms. Assessment of the algorithm performance was conducted in an independent prospective cohort of 271 MS patients from a single centre. Results: We found algorithms for predicting confirmed disability accumulation for the different scales, no evidence of disease activity (NEDA), onset of immunotherapy and the escalation from low- to high-efficacy therapy with intermediate to high-accuracy. This accuracy was achieved for most of the predictors using clinical data alone or in combination with imaging data. Still, in some cases, the addition of omics data slightly increased algorithm performance. Accuracies were comparable in both cohorts. Conclusion: Combining clinical, imaging and omics data with machine learning helps identify MS patients at risk of disability worsening.
- dc.format.mimetype application/pdf
- dc.identifier.citation Andorra M, Freire A, Zubizarreta I, de Rosbo NK, Bos SD, Rinas M, et al. Predicting disease severity in multiple sclerosis using multimodal data and machine learning. J Neurol. 2024 Mar;271(3):1133-49. DOI: 10.1007/s00415-023-12132-z
- dc.identifier.doi http://dx.doi.org/10.1007/s00415-023-12132-z
- dc.identifier.issn 0340-5354
- dc.identifier.uri http://hdl.handle.net/10230/68349
- dc.language.iso eng
- dc.publisher Springer
- dc.relation.ispartof J Neurol. 2024 Mar;271(3):1133-49
- dc.rights © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword Imaging
- dc.subject.keyword Machine learning
- dc.subject.keyword Multiple sclerosis
- dc.subject.keyword Omics
- dc.subject.keyword Precision medicine
- dc.title Predicting disease severity in multiple sclerosis using multimodal data and machine learning
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion