In this work, we present a framework for the interpretable analysis of machine learning algorithms to predict the Multiple Sclerosis worsening using the datasets provided by the iDPP@ CLEF 2023 Challenge. The proposed framework is modular and allows to investigate the link between the provided static and dynamic features and the outcome to be predicted. Our findings show that better performance could be achieved by using Random Survival Forests together with temporal information about the clinical scores and a proposed feature related to the normalized frequency of patients’ relapses.
Time-to-event interpretable machine learning for multiple sclerosis worsening prediction: results from iDPP@ CLEF 2023 / Lombardi, Angela; Luigia Natalia De Bonis, Maria; Fasano, Giuseppe; Sportelli, Alessia; Colafiglio, Tommaso; Lofù, Domenico; Sorino, Paolo; Narducci, Fedelucio; Di Sciascio, Eugenio; Di Noia, Tommaso. - (2023). (Intervento presentato al convegno iDPP@ CLEF 2023 tenutosi a Grecia).
Time-to-event interpretable machine learning for multiple sclerosis worsening prediction: results from iDPP@ CLEF 2023
Angela Lombardi;Giuseppe Fasano;Tommaso Colafiglio;
2023
Abstract
In this work, we present a framework for the interpretable analysis of machine learning algorithms to predict the Multiple Sclerosis worsening using the datasets provided by the iDPP@ CLEF 2023 Challenge. The proposed framework is modular and allows to investigate the link between the provided static and dynamic features and the outcome to be predicted. Our findings show that better performance could be achieved by using Random Survival Forests together with temporal information about the clinical scores and a proposed feature related to the normalized frequency of patients’ relapses.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
