PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE

Alitto, AR, Gatta, R, Vanneste, BGL, Vallati, Mauro, Meldolesi, E, Damiani, A, Lanzotti, V, Mattiucci, GC, Frascino, V, Masciocchi, C, Catucci, F, Dekker, A, Lambin, P, Valentini, V and Mantini, G (2017) PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE. Future Oncology, 13 (24). ISSN 1479-6694

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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 from cancerdata.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.

Item Type:	Article
Uncontrolled Keywords:	Decision Support System, individualized medicine, large database, machine learning, ontology, predictive model
Subjects:	Q Science > QA Mathematics > QA76 Computer software R Medicine > R Medicine (General) T Technology > T Technology (General)
Schools:	School of Computing and Engineering
Related URLs:	Author
Depositing User:	Sally Hughes
Date Deposited:	13 Dec 2017 09:30
Last Modified:	31 Jul 2018 01:38
URI:	http://eprints.hud.ac.uk/id/eprint/34082

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