A machine learning pipeline for multiple sclerosis course detection from clinical scales and patient reported outcomes.
Annu Int Conf IEEE Eng Med Biol Soc
; 2015: 4443-6, 2015 Aug.
Article
em En
| MEDLINE
| ID: mdl-26737281
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.
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Base de dados:
MEDLINE
Assunto principal:
Esclerose Múltipla
Idioma:
En
Ano de publicação:
2015
Tipo de documento:
Article