Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression

Edward De Brouwer, Thijs Becker, Yves Moreau, Eva Kubala Havrdova, Maria Trojano, Sara Eichau, Serkan Ozakbas, Marco Onofrj, Pierre Grammond, Jens Kuhle, Ludwig Kappos, Patrizia Sola, Elisabetta Cartechini, Jeannette Lechner-Scott, Raed Alroughani, Oliver Gerlach, Tomas Kalincik, Franco Granella, Francois Grand'Maison, Roberto BergamaschiMaria José Sá, Bart Van Wijmeersch, Aysun Soysal, Jose Luis Sanchez-Menoyo, Claudio Solaro, Cavit Boz, Gerardo Iuliano, Katherine Buzzard, Eduardo Aguera-Morales, Murat Terzi, Tamara Castillo Trivio, Daniele Spitaleri, Vincent Van Pesch, Vahid Shaygannejad, Fraser Moore, Celia Oreja-Guevara, Davide Maimone, Riadh Gouider, Tunde Csepany, Cristina Ramo-Tello, Liesbet Peeters

Research output: Contribution to journalArticleResearchpeer-review

22 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number106180
Number of pages14
JournalComputer Methods and Programs in Biomedicine
Volume208
DOIs
Publication statusPublished - Sept 2021
Externally publishedYes

Keywords

  • Disability progression
  • Electronic health records
  • Longitudinal data
  • Machine learning
  • Multiple sclerosis
  • Real-world data
  • Recurrent neural networks

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