Longitudinal Machine Learning Modeling of Ms Patient Trajectories Improves Predictions of Disability Progression Publisher Pubmed



De Brouwer E¹ ; Becker T² ; Moreau Y¹ ; Havrdova EK⁴ ; Trojano M⁵ ; Eichau S⁶ ; Ozakbas S⁷ ; Onofrj M⁸ ; Grammond P⁹ ; Kuhle J¹⁰ ; Kappos L¹⁰ ; Sola P¹¹ ; Cartechini E¹² ; Lechnerscott J¹³ Show All Authors
Source: Computer Methods and Programs in Biomedicine Published:2021

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. © 2021