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Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression
E. De Brouwer, T. Becker, Y. Moreau, EK. Havrdova, M. Trojano, S. Eichau, S. Ozakbas, M. Onofrj, P. Grammond, J. Kuhle, L. Kappos, P. Sola, E. Cartechini, J. Lechner-Scott, R. Alroughani, O. Gerlach, T. Kalincik, F. Granella, F. Grand'Maison, R....
Language English Country Ireland
Document type Journal Article
- MeSH
- Humans MeSH
- Neural Networks, Computer MeSH
- Multiple Sclerosis * MeSH
- Machine Learning * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
BACKGROUND AND OBJECTIVES: Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work we aim to predict disability progression by optimally extracting information from longitudinal patient data in the real-world setting, with a special focus on the sporadic sampling problem. METHODS: We use machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization. A subset of 6682 patients from the MSBase registry is used. RESULTS: We can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.85, which represents a 32% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. CONCLUSIONS: Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction and represents a step forward towards AI-assisted precision medicine in MS.
1 Biostat Data Science Institute Hasselt University Diepenbeek Belgium
Azienda Ospedaliera di Rilievo Nazionale San Giuseppe Moscati Avellino Avellino Italy
Azienda Ospedaliera Universitaria Modena Italy
Azienda Sanitaria Unica Regionale Marche AV3 Macerata Italy
Bakirkoy Education and Research Hospital for Psychiatric and Neurological Diseases Istanbul Turkey
Box Hill Hospital Melbourne Australia
Charles University Prague General University Hospital Prague Czech
CISSS Chaudire Appalache Levis Canada
Cliniques Universitaires Saint Luc Brussels Belgium
CORe Department of Medicine University of Melbourne Melbourne Australia
Department of Basic Medical Sciences Neuroscience and Sense Organs University of Bari Bari Italy
Department of Immunology Biomedical Research Institute Hasselt University Diepenbeek 3590 Belgium
Dept of Rehabilitation mons L Novarese Hospital Moncrivello Italy
Dokuz Eylul University Konak Izmir Turkey
ESAT STADIUS KU Leuven Leuven 3001 Belgium
Garibaldi Hospital Catania Italy
Hospital Clinico San Carlos Madrid Spain
Hospital de Galdakao Usansolo Galdakao Spain
Hospital Germans Trias i Pujol Badalona Spain
Hospital Universitario Donostia San Sebastain Spain
Hospital Universitario Virgen Macarena Sevilla Spain
IRCCS Mondino Foundation Pavia Italy
Isfahan Neurosciences Research Center Isfahan University of Medical Sciences Isfahan Iran
Jewish General Hospital Montreal Canada
KTU Medical Faculty Farabi Hospital Trabzon Turkey
Mayis University Samsun Turkey
Melbourne MS Centre Department of Neurology Royal Melbourne Hospital Melbourne Australia
previously at Ospedali Riuniti di Salerno Salerno Italy
Rehabilitation and MS Centre Overpelt Hasselt University Hasselt Belgium
University G d'Annunzio Chieti Italy
University Hospital Reina Sofia Cordoba Spain
University Newcastle Newcastle Australia
University of Debrecen Debrecen Hungary
References provided by Crossref.org
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