PRODIGE: PRediction models in prOstate cancer for personalized meDIcine challenGE

  • Anna Rita Alitto
  • , R. Gatta
  • , Bgl Vanneste
  • , M. Vallati
  • , Elisa Meldolesi
  • , Andrea Damiani
  • , V. Lanzotti
  • , Gian Carlo Mattiucci
  • , Vincenzo Frascino
  • , Carlotta Masciocchi
  • , F. Catucci
  • , A. Dekker
  • , P. Lambin
  • , Vincenzo Valentini
  • , Giovanna Mantini

Risultato della ricerca: Contributo in rivistaArticolo

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

OSS delle Nazioni Unite

Questo processo contribuisce al raggiungimento dei seguenti obiettivi di sviluppo sostenibile

  1. SDG 3 - Salute e benessere
    SDG 3 Salute e benessere

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

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