| Autor: |
Denissen S; AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium.; icometrix, 3012 Leuven, Belgium., Chén OY; Faculty of Social Sciences and Law, University of Bristol, Bristol BS8 1QU, UK.; Department of Engineering, University of Oxford, Oxford OX1 3PJ, UK., De Mey J; AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium.; Department of Radiology, UZ Brussel, Vrije Universiteit Brussel, 1090 Brussels, Belgium., De Vos M; Faculty of Engineering Science, KU Leuven, 3001 Leuven, Belgium.; Faculty of Medicine, KU Leuven, 3001 Leuven, Belgium., Van Schependom J; AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium.; Department of Electronics and Informatics (ETRO), Vrije Universiteit Brussel, 1050 Brussels, Belgium., Sima DM; AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium.; icometrix, 3012 Leuven, Belgium., Nagels G; AIMS Laboratory, Center for Neurosciences, UZ Brussel, Vrije Universiteit Brussel, 1050 Brussels, Belgium.; icometrix, 3012 Leuven, Belgium.; St Edmund Hall, Queen's Ln, Oxford OX1 4AR, UK. |
| Abstrakt: |
Multiple sclerosis (MS) manifests heterogeneously among persons suffering from it, making its disease course highly challenging to predict. At present, prognosis mostly relies on biomarkers that are unable to predict disease course on an individual level. Machine learning is a promising technique, both in terms of its ability to combine multimodal data and through the capability of making personalized predictions. However, most investigations on machine learning for prognosis in MS were geared towards predicting physical deterioration, while cognitive deterioration, although prevalent and burdensome, remained largely overlooked. This review aims to boost the field of machine learning for cognitive prognosis in MS by means of an introduction to machine learning and its pitfalls, an overview of important elements for study design, and an overview of the current literature on cognitive prognosis in MS using machine learning. Furthermore, the review discusses new trends in the field of machine learning that might be adopted for future studies in the field. |
