Konferenzbeitrag

A Multi-Label Machine Learning Approach to Support Pathologist's Histological Analysis

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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.

Language
Englisch

Bibliographic citation
In: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019 ; Year: 2019 ; Pages: 197-208 ; Zagreb: IRENET - Society for Advancing Innovation and Research in Economy

Classification
Wirtschaft
Health: General
Health Behavior
Subject
machine learning
health problems
knowledge extraction
data mining
classification

Event
Geistige Schöpfung
(who)
Azzini, Antonia
Cortesi, Nicola
Marrara, Stefania
Topalović, Amir
Event
Veröffentlichung
(who)
IRENET - Society for Advancing Innovation and Research in Economy
(where)
Zagreb
(when)
2019

Handle
Last update
10.03.2025, 11:44 AM CET

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Object type

  • Konferenzbeitrag

Associated

  • Azzini, Antonia
  • Cortesi, Nicola
  • Marrara, Stefania
  • Topalović, Amir
  • IRENET - Society for Advancing Innovation and Research in Economy

Time of origin

  • 2019

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