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 |
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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: | |
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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