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

Antonia Azzini, Nicola Cortesi, Stefania Marrara and Amir Topalović

A chapter in Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019, 2019, pp 197-208 from IRENET - Society for Advancing Innovation and Research in Economy, Zagreb

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.

Keywords: machine learning; health problems; knowledge extraction; data mining; classification (search for similar items in EconPapers)
JEL-codes: I10 I12 (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:entr19:207680

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