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

In: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019

Author

Listed:
  • Azzini, Antonia
  • Cortesi, Nicola
  • Marrara, Stefania
  • Topalović, Amir

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.

Suggested Citation

  • Azzini, Antonia & Cortesi, Nicola & Marrara, Stefania & Topalović, Amir, 2019. "A Multi-Label Machine Learning Approach to Support Pathologist's Histological Analysis," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2019), Rovinj, Croatia, in: Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019, pages 197-208, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb.
    • Handle: RePEc:zbw:entr19:207680
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    File URL: https://www.econstor.eu/bitstream/10419/207680/1/24-ENT-2019-Azzini-et-al-197-208.pdf
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    References listed on IDEAS

    as
    1. Grigorios Tsoumakas & Ioannis Katakis, 2007. "Multi-Label Classification: An Overview," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 3(3), pages 1-13, July.
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    More about this item

    Keywords

    machine learning; health problems; knowledge extraction; data mining; classification;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • I12 - Health, Education, and Welfare - - Health - - - Health Behavior

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