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.
Alitto, A. R., Gatta, R., Vanneste, B., Vallati, M., Meldolesi, E., Damiani, A., Lanzotti, V., Mattiucci, G. C., Frascino, V., Masciocchi, C., Catucci, F., Dekker, A., Lambin, P., Valentini, V., Mantini, G., PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE, <<FUTURE ONCOLOGY>>, 2017; 13 (24): 2171-2181. [doi:10.2217/fon-2017-0142] [http://hdl.handle.net/10807/111833]
PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE
Alitto, Anna Rita;Gatta, Roberto;Meldolesi, Elisa;Damiani, Andrea;Mattiucci, Gian Carlo;Frascino, Vincenzo;Masciocchi, Carlotta;Catucci, Francesco;Dekker, Alain;Valentini, Vincenzo;Mantini, Giovanna
2017
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.
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