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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.

References

  1. Adam, D.: A guide to R - the pandemic’s misunderstood metric. Nature 583(7816), 346–348 (2020)

    Article  Google Scholar 

  2. Donoho, S., Rendell, L.: Feature construction using fragmentary knowledge. In: Liu, H., Motoda, H. (eds.) Feature Extraction, Construction and Selection. SECS, vol. 453, pp. 273–288. Springer, Boston (1998). https://doi.org/10.1007/978-1-4615-5725-8_17

    Chapter  Google Scholar 

  3. Elmasri, R., Navathe, S.B.: The Enhanced Entity-Relationship (EER) Model, pp. 107–135. Addison-Wesley, Boston (2000)

    Google Scholar 

  4. Feurer, M., Eggensperger, K., Falkner, S., Lindauer, M., Hutter, F.: Auto-sklearn 2.0: hands-free autoML via meta-learning. arXiv preprint arXiv:2007.04074 (2020)

  5. Gabrilovich, E., Markovitch, S.: Wikipedia-based semantic interpretation for natural language processing. J. Artif. Intell. Res. 34, 443–498 (2009)

    Article  MATH  Google Scholar 

  6. Galhotra, S., Khurana, U., Hassanzadeh, O., Srinivas, K., Samulowitz, H., Qi, M.: Automated feature enhancement for predictive modeling using external knowledge. In: 2019 International Conference Data Mining Workshops, pp. 1094–1097. IEEE (2019)

    Google Scholar 

  7. Garg, A., Mago, V.: Role of machine learning in medical research: a survey. Comput. Sci. Rev. 40, 100370 (2021)

    Article  MathSciNet  Google Scholar 

  8. Hu, Y.J., Kibler, D.: Generation of attributes for learning algorithms. In: AAAI/IAAI, vol. 1, pp. 806–811 (1996)

    Google Scholar 

  9. Hutter, F., Kotthoff, L., Vanschoren, J.: Automated Machine Learning: Methods, Systems, Challenges. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-05318-5

    Book  Google Scholar 

  10. Markovitch, S., Rosenstein, D.: Feature generation using general constructor functions. Mach. Learn. 49, 59–98 (2004)

    Article  MATH  Google Scholar 

  11. Momeni, Z., Hassanzadeh, E., Abadeh, M.S., Bellazzi, R.: A survey on single and multi omics data mining methods in cancer data classification. J. Biomed. Inform. 107, 103466 (2020)

    Article  Google Scholar 

  12. Salguero, A.G., Medina, J., Delatorre, P., Espinilla, M.: Methodology for improving classification accuracy using ontologies: application in the recognition of activities of daily living. J. Ambient Intell. Hum. Comput. 10(6), 2125–2142 (2019)

    Article  Google Scholar 

  13. Shilo, S., Rossman, H., Segal, E.: Axes of a revolution: challenges and promises of big data in healthcare. Nat. Med. 26(1), 29–38 (2020)

    Article  Google Scholar 

  14. Waring, J., Lindvall, C., Umeton, R.: Automated machine learning: review of the state-of-the-art and opportunities for healthcare. Artif. Intell. Med. 104, 101822 (2020)

    Article  Google Scholar 

  15. Yang, H., Bath, P.A.: The use of data mining methods for the prediction of dementia: evidence from the English longitudinal study of aging. IEEE J. Biomed. Health Inform. 24(2), 345–353 (2020)

    Article  Google Scholar 

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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 .

Editor information

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

  • eBook Packages: Computer ScienceComputer Science (R0)

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