PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE. (October 2017)

• Record Type: Journal Article
• Authors: Alitto, AR; Gatta, R; Vanneste, BGL; Vallati, M; Meldolesi, E; Damiani, A; Lanzotti, V; Mattiucci, GC; Frascino, V; Masciocchi, C; Catucci, F; Dekker, A; Lambin, P; Valentini, V; Mantini, G
• Is Part Of: Future oncology. Volume 13:Number 24(2017)
• Page Start: 2171
• Page End: 2181
• Publication Date: 2017-10
• Subjects: Decision Support System -- individualized medicine -- large database -- machine learning -- ontology -- predictive model; Oncology -- Periodicals; 616.99405
• DOI: 10.2217/fon-2017-0142
• Languages: English
• ISSNs: 1479-6694

Abstract

Aim: Identifying the best care for a patient can be extremely challenging. To support the creation of multifactorial Decision Support Systems (DSSs), we propose an Umbrella Protocol, focusing on prostate cancer.Materials & methods: The PRODIGE project consisted of a workflow for standardizing data, and procedures, to create a consistent dataset useful to elaborate DSSs. Techniques from classical statistics and machine learning will be adopted. The general protocol accepted by our Ethical Committee can be downloaded fromcancerdata.org .Results: A standardized knowledge sharing process has been implemented by using a semi-formal ontology for the representation of relevant clinical variables.Conclusion: The development of DSSs, based on standardized knowledge, could be a tool to achieve a personalized decision-making.