This paper introduces the Greg, ML platform, a machine-learning engine and toolset conceived to generate automatic diagnostic suggestions based on patient profiles. Greg, ML departs from many other experiences in machine learning for healthcare in the fact that it was designed to handle a large number of different diagnoses, in the order of the hundreds. We discuss the architecture that stands at the core of Greg, ML, designed to handle the complex challenges posed by this ambitious goal, and confirm its effectiveness with experimental results based on the working prototype we have developed. Finally, we discuss challenges and opportunities related to the use of this kind of tools in medicine, and some important lessons learned while developing the tool. In this respect, we underline that Greg, ML should be conceived primarily as a support for expert doctors in their diagnostic decisions, and can hardly replace humans in their judgment.

Greg, ML – Machine Learning for Healthcare at a Scale

Mecca, Giansalvatore;Santoro, Donatello;Veltri, Enzo
2020

Abstract

This paper introduces the Greg, ML platform, a machine-learning engine and toolset conceived to generate automatic diagnostic suggestions based on patient profiles. Greg, ML departs from many other experiences in machine learning for healthcare in the fact that it was designed to handle a large number of different diagnoses, in the order of the hundreds. We discuss the architecture that stands at the core of Greg, ML, designed to handle the complex challenges posed by this ambitious goal, and confirm its effectiveness with experimental results based on the working prototype we have developed. Finally, we discuss challenges and opportunities related to the use of this kind of tools in medicine, and some important lessons learned while developing the tool. In this respect, we underline that Greg, ML should be conceived primarily as a support for expert doctors in their diagnostic decisions, and can hardly replace humans in their judgment.
File in questo prodotto:
File Dimensione Formato  
35.HEALTH2020.pdf

accesso aperto

Tipologia: Pdf editoriale
Licenza: DRM non definito
Dimensione 1.6 MB
Formato Adobe PDF
1.6 MB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11563/144003
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 2
  • ???jsp.display-item.citation.isi??? 0
social impact