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Automatic Exploration of Domain Knowledge in Healthcare

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Intelligent Data Engineering and Automated Learning – IDEAL 2022 (IDEAL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13756))

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Abstract

Throughout the years, healthcare has been one of the privileged areas to apply the information discovery process, empowering and supporting medical staff on their daily activities. One of the main reasons for its success is the availability of medical expertise, which can be incorporated in training models to reach higher levels of performance. While this has been done painfully and manually, during the preparation step, it has become hindered with the advent of AutoML. In this paper, we present the automation of data preparation and feature engineering, while exploring domain knowledge represented through extended entity-relationship (EER) diagrams. A COVID-19 case study shows that our automation outperforms existing AutoML tools, such as auto-sklearn [4], both in quality of the models and processing times.

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Notes

  1. 1.

    https://www.ecdc.europa.eu/en/covid-19/data.

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Acknowledgments

This work was supported by national funds by Fundação para a Ciência e Tecnologia (FCT) through project VizBig (PTDC/CCI-CIF/28939/2017).

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Authors and Affiliations

  1. Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais 1, Lisboa, Portugal

    Tiago Afonso & Cláudia Antunes

Authors
  1. Tiago Afonso
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  2. Cláudia Antunes
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Corresponding author

Correspondence to Cláudia Antunes .

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Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

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Afonso, T., Antunes, C. (2022). Automatic Exploration of Domain Knowledge in Healthcare. In: Yin, H., Camacho, D., Tino, P. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2022. IDEAL 2022. Lecture Notes in Computer Science, vol 13756. Springer, Cham. https://doi.org/10.1007/978-3-031-21753-1_8

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  • DOI: https://doi.org/10.1007/978-3-031-21753-1_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21752-4

  • Online ISBN: 978-3-031-21753-1

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