PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE

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.

Keywords: Decision Support System; individualized medicine; large database; machine learning; ontology; predictive model.