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A Multi-Label Machine Learning Approach to Support Pathologist's Histological Analysis
Antonia Azzini
; Consortium for the Technology Transfer – C2T, Italy
Nicola Cortesi
; Consortium for the Technology Transfer – C2T, Italy
Stefania Marrara
; Consortium for the Technology Transfer – C2T, Italy
Amir Topalović
; Consortium for the Technology Transfer – C2T, Italy
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.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Keywords
Hrčak ID:
251010
URI
Publication date:
31.10.2019.
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