FIORINI , Samuele

Data are influencing every aspect of our lives, from our work activities, to our spare time and even to our health. In this regard, medical diagnosis and treatments are often supported by quantitative measures and observations, such as laboratory tests, medical imaging or genetic analysis. In medicine, as well as in several other scientific...

The main goal of supervised data analytics is to model a target phenomenon given a limited amount of samples, each represented by an arbitrarily large number of variables. Especially when the number of variables is much larger than the number of available samples, variable selection is a key step as it allows to identify a possibly reduced...

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....

Background: Achieving an accurate clinical course description in Multiple Sclerosis (MS) is a very hard task even for clinical experts, but it is crucial for communication, prognosis, treatment decision-making, design and recruitment of clinical trials. In this context, meaningful data being "hidden" into Patient Centered Outcomes (PCOs), could...