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Simple Explanations to Summarise Subgroup Discovery Outcomes: A Case Study Concerning Patient Phenotyping

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Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1752))

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Abstract

Phenotyping is essential in medical research, as it provides a better understanding of healthcare problems owing to the fact that clinical phenotypes identify subsets of patients with common characteristics. Subgroup discovery (SD) appears to be a promising machine learning approach because it provides a framework with which to search for interesting subgroups according to the relations between the individual characteristics and a target value. Each single pattern extracted by SD algorithms is human-readable. However, its complexity (the number of attributes involved) and the high number of subgroups obtained make the overall model difficult to understand. In this work, we propose a method with which to explain SD, designed for the clinical context. We have employed a two-step process in order to obtain SD model-agnostic explanations based on a decision tree surrogate model. The complexity involved in evaluating explainable methods led us to adopt a multiple strategy. We first show how explanations are built, and test a selection of state-of-the-art SD algorithms and gold-standard datasets. We then illustrate the suitability of the method in a clinical use case for an antimicrobial resistance problem. Finally, we study the utility of the method by surveying a small group in order to validate it from a human-centric perspective.

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Notes

  1. 1.

    Available at PyPI, https://pypi.org/project/subgroups/.

  2. 2.

    Available at GitHub, https://github.com/Enrique-Val/SubgroupExplainer.

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Acknowledgement

This work was partially funded by the CONFAINCE project (Ref: PID2021-122194OB-I00), supported by the Spanish Ministry of Science and Innovation, the Spanish Agency for Research and the IMPACT-T2D project (PMP21/00092) supported by the Spanish Health Institute Carlos III (ISCIII).

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Authors and Affiliations

  1. Technical University of Madrid, Madrid, Spain

    Enrique Valero-Leal

  2. AIKE Group (INTICO), University of Murcia, Murcia, Spain

    Enrique Valero-Leal, Manuel Campos & Jose M. Juarez

  3. Murcian Bio-Health Institute (IMIB-Arrixaca), Murcia, Spain

    Manuel Campos

Authors
  1. Enrique Valero-Leal
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  2. Manuel Campos
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  3. Jose M. Juarez
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Correspondence to Jose M. Juarez .

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Valero-Leal, E., Campos, M., Juarez, J.M. (2023). Simple Explanations to Summarise Subgroup Discovery Outcomes: A Case Study Concerning Patient Phenotyping. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1752. Springer, Cham. https://doi.org/10.1007/978-3-031-23618-1_29

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  • DOI: https://doi.org/10.1007/978-3-031-23618-1_29

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  • Online ISBN: 978-3-031-23618-1

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