PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE

Future Oncol. 2017 Oct;13(24):2171-2181. doi: 10.2217/fon-2017-0142. Epub 2017 Jul 31.

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.

MeSH terms

  • Decision Support Systems, Clinical*
  • Humans
  • Machine Learning
  • Male
  • Medical Informatics / methods*
  • Precision Medicine* / methods
  • Prognosis
  • Prostatic Neoplasms / diagnosis*
  • Software*
  • Workflow