Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/207680
Authors: 
Azzini, Antonia
Cortesi, Nicola
Marrara, Stefania
Topalović, Amir
Year of Publication: 
2019
Citation: 
[Title:] Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019 [ISSN:] 2706-4735 [Volume:] 5 [Pages:] 197-208
Abstract: 
This paper proposes a new tool in the field of telemedicine, defined as a specific branch where IT supports medicine, in case distance impairs the proper care to be delivered to a patient. All the information contained into medical texts, if properly extracted, may be suitable for searching, classification, or statistical analysis. For this reason, in order to reduce errors and improve quality control, a proper information extraction tool may be useful. In this direction, this work presents a Machine Learning Multi-Label approach for the classification of the information extracted from the pathology reports into relevant categories. The aim is to integrate automatic classifiers to improve the current workflow of medical experts, by defining a Multi- Label approach, able to consider all the features of a model, together with their relationships.
Subjects: 
machine learning
health problems
knowledge extraction
data mining
classification
JEL: 
I10
I12
Document Type: 
Conference Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.