title: A machine learning pipeline for multiple sclerosis course detection from clinical scales and patient reported outcomes.
abstract: In this work we present a machine learning pipeline for the detection of multiple sclerosis course from a collection of inexpensive and non-invasive measures such as clinical scales and patient-reported outcomes. The proposed analysis is conducted on a dataset coming from a clinical study comprising 457 patients affected by multiple sclerosis. The 91 collected variables describe patients mobility, fatigue, cognitive performance, emotional status, bladder continence and quality of life. A preliminary data exploration phase suggests that the group of patients diagnosed as Relapsing-Remitting can be isolated from other clinical courses. Supervised learning algorithms are then applied to perform feature selection and course classification. Our results confirm that clinical scales and patient-reported outcomes can be used to classify Relapsing-Remitting patients.
authors.forename: Samuele
authors.initials: S
authors.lastname: Fiorini
authors.forename: Alessandro
authors.initials: A
authors.lastname: Verri
authors.forename: Andrea
authors.initials: A
authors.lastname: Tacchino
authors.forename: Michela
authors.initials: M
authors.lastname: Ponzio
authors.forename: Giampaolo
authors.initials: G
authors.lastname: Brichetto
authors.forename: Annalisa
authors.initials: A
authors.lastname: Barla
mesh_terms: D006801:Humans; D000069550:Machine Learning; D009103:Multiple Sclerosis; D000071066:Patient Reported Outcome Measures; D011788:Quality of Life