PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE

Gian Carlo Mattiucci, Vincenzo Valentini, Anna Rita Alitto, Elisa Meldolesi, Andrea Damiani, Vincenzo Frascino, Carlotta Masciocchi, Giovanna Mantini, Bgl Vanneste, M. Vallati, A. Dekker, P. Lambin

Risultato della ricerca: Contributo in rivistaArticolo in rivista

7 Citazioni (Scopus)

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.
Lingua originaleEnglish
pagine (da-a)2171-2181
Numero di pagine11
RivistaFuture Oncology
Volume13
DOI
Stato di pubblicazionePubblicato - 2017

Keywords

  • Decision Support System
  • Decision Support Systems, Clinical
  • Humans
  • Machine Learning
  • Male
  • Medical Informatics
  • Precision Medicine
  • Prognosis
  • Prostatic Neoplasms
  • Software
  • Workflow
  • individualized medicine
  • large database
  • machine learning
  • ontology
  • predictive model

Fingerprint

Entra nei temi di ricerca di 'PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE'. Insieme formano una fingerprint unica.

Cita questo