Temporal prediction of multiple sclerosis evolution from patient-centered outcomes

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Samuele Fiorini, Alessandro Verri, Annalisa Barla, Andrea Tacchino, Giampaolo Brichetto ;
Proceedings of the 2nd Machine Learning for Healthcare Conference, PMLR 68:112-125, 2017.

Abstract

Multiple Sclerosis is a degenerative condition of the central nervous system that affects nearly 2.5 million of individuals in terms of their physical, cognitive, psychological and social capabilities. Despite the high variability of its clinical presentation, relapsing and progressive multiple sclerosis are considered the two main disease types, with the former possibly evolving into the latter. Recently, the attention of the medical community toward the use of patient-centered outcomes in multiple sclerosis has significantly increased. Such patient-friendly measures are devoted to the assessment of the impact of the disease on several domains of the patient life. In this work, we investigate on use of patient-centered outcomes to predict the evolution of the disease and to assess its impact on patients’ lives. To this aim, we build a novel temporal model based on gradient boosting classification and multiple-output elastic-net regression. The model provides clinically interpretable results along with accurate predictions of the disease course evolution.

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