Longitudinal modeling of MS patient trajectories improves predictions of disability progression
Abstract
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 address the task of optimally extracting information from longitudinal patient data in the real-world setting with a special focus on the sporadic sampling problem. Using the MSBase registry, we show that with machine learning methods suited for patient trajectories modeling, such as recurrent neural networks and tensor factorization, we can predict disability progression of patients in a two-year horizon with an ROC-AUC of 0.86, which represents a 33% decrease in the ranking pair error (1-AUC) compared to reference methods using static clinical features. Compared to the models available in the literature, this work uses the most complete patient history for MS disease progression prediction.
Keywords
Cite
@article{arxiv.2011.04749,
title = {Longitudinal modeling of MS patient trajectories improves predictions of disability progression},
author = {Edward De Brouwer and Thijs Becker and Yves Moreau and Eva Kubala Havrdova and Maria Trojano and Sara Eichau and Serkan Ozakbas and Marco Onofrj and Pierre Grammond and Jens Kuhle and Ludwig Kappos and Patrizia Sola and Elisabetta Cartechini and Jeannette Lechner-Scott and Raed Alroughani and Oliver Gerlach and Tomas Kalincik and Franco Granella and Francois GrandMaison and Roberto Bergamaschi and Maria Jose Sa and Bart Van Wijmeersch and Aysun Soysal and Jose Luis Sanchez-Menoyo and Claudio Solaro and Cavit Boz and Gerardo Iuliano and Katherine Buzzard and Eduardo Aguera-Morales and Murat Terzi and Tamara Castillo Trivio and Daniele Spitaleri and Vincent Van Pesch and Vahid Shaygannej and Fraser Moore and Celia Oreja Guevara and Davide Maimone and Riadh Gouider and Tunde Csepany and Cristina Ramo-Tello and Liesbet Peeters},
journal= {arXiv preprint arXiv:2011.04749},
year = {2020}
}