Recently machine-learning techniques have been applied in a variety of fields. One of the most promising and challenging is handling medical records. In this paper we present Greg, ML, a machine-learning tool for generating automatic diagnostic suggestions based on patient profiles. At the core of our system there are two machine learning classifiers: a natural-language module that handles reports of instrumental exams, and a profile classifier that outputs diagnostic suggestions to the doctor. After discussing the architecture we present some experimental results based on the working prototype we have developed. Finally, we examine challenges and opportunities related to the use of this kind of tools in medicine, and some important lessons learned developing the tool. In this respect, despite the ironic title of this paper, we underline that Greg should be conceived primarily as a support for expert doctors in their diagnostic decisions, and can hardly replace humans in their judgment.

Greg, ML: Automatic diagnostic suggestions humanity is overrated. Or not

Mecca, Giansalvatore;Santoro, Donatello;Veltri, Enzo
2019-01-01

Abstract

Recently machine-learning techniques have been applied in a variety of fields. One of the most promising and challenging is handling medical records. In this paper we present Greg, ML, a machine-learning tool for generating automatic diagnostic suggestions based on patient profiles. At the core of our system there are two machine learning classifiers: a natural-language module that handles reports of instrumental exams, and a profile classifier that outputs diagnostic suggestions to the doctor. After discussing the architecture we present some experimental results based on the working prototype we have developed. Finally, we examine challenges and opportunities related to the use of this kind of tools in medicine, and some important lessons learned developing the tool. In this respect, despite the ironic title of this paper, we underline that Greg 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  
34.SEBD2019.pdf

accesso aperto

Tipologia: Documento in Post-print
Licenza: DRM non definito
Dimensione 1.36 MB
Formato Adobe PDF
1.36 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: https://hdl.handle.net/11563/140546
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact