Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course. A proof-of-principle study / Tacchella, Andrea; Romano, Silvia; Ferraldeschi, Michela; Salvetti, Marco; Zaccaria, Andrea; Crisanti, Andrea; Grassi, Francesca. - In: F1000RESEARCH. - ISSN 2046-1402. - 6:(2018), pp. 1-12. [10.12688/f1000research.13114.1]

Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course. A proof-of-principle study

Tacchella, Andrea
Co-primo
;
Romano, Silvia
Co-primo
;
Ferraldeschi, Michela;Salvetti, Marco;Crisanti, Andrea;Grassi, Francesca
2018

Abstract

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options. Approaches to improve clinical decisions, such as collective intelligence of human groups and machine learning algorithms are widely investigated. Methods: Medical students and a machine learning algorithm predicted the course of disease on the basis of randomly chosen clinical records of patients that attended at the Multiple Sclerosis service of Sant'Andrea hospital in Rome. Results: A significant improvement of predictive ability was obtained when predictions were combined with a weight that depends on the consistence of human (or algorithm) forecasts on a given clinical record. Conclusions: In this work we present proof-of-principle that human-machine hybrid predictions yield better prognoses than machine learning algorithms or groups of humans alone. To strengthen this preliminary result, we propose a crowdsourcing initiative to collect prognoses by physicians on an expanded set of patients.
2018
crowdsourcing; hybrid predictions; machine learning; multiple sclerosis; random forest; collective intelligence; biochemistry, genetics and molecular biology; immunology and microbiology; pharmacology; toxicology and pharmaceutics
01 Pubblicazione su rivista::01a Articolo in rivista
Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course. A proof-of-principle study / Tacchella, Andrea; Romano, Silvia; Ferraldeschi, Michela; Salvetti, Marco; Zaccaria, Andrea; Crisanti, Andrea; Grassi, Francesca. - In: F1000RESEARCH. - ISSN 2046-1402. - 6:(2018), pp. 1-12. [10.12688/f1000research.13114.1]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1131801
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