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Population and Individual Level Meal Response Patterns in Continuous Glucose Data

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Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1602))

Abstract

Diabetes research has changed with the introduction of wearables that are able to continuously collect physiological data (e.g., blood glucose levels), which has allowed for data-driven solutions. In this context, patients are still expected to self-record events tied to their daily routines (e.g., meals). Since self-recording is prone to errors, automatic detection of meal events could improve the quality of event data and reduce registration burden. In this paper, we investigate the feasibility of meal detection from continuous glucose data by using population level data compared to individual data. We discuss the advantages and disadvantages of both approaches based on a method to identify patterns in time series that can be used to map the characteristics of a glucose signal response to a meal event. Event responses, i.e., subsequences that come right after a recorded event, are identified and fuzzy clustering is used to group different types of them. Our results indicate that both population and individual data give comparable results, which suggests that both could be used interchangeably to develop event identification models.

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Notes

  1. 1.

    smarthealth.cs.ohio.edu/OhioT1DM-dataset.html.

  2. 2.

    http://github.com/omadson/fuzzy-c-means.

  3. 3.

    http://github.com/TDAmeritrade/stumpy.

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Acknowledgment

This publication is part of the project DiaGame (with project number 628.011.027) of the research programme Data2Person which is (partly) financed by the Dutch Research Council (NWO).

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Authors and Affiliations

  1. Jheronimus Academy of Data Science, Eindhoven University of Technology, Eindhoven, The Netherlands

    Danilo Ferreira de Carvalho & Uzay Kaymak

  2. Industrial Engineering and Innovation Sciences, Eindhoven University of Technology, Eindhoven, The Netherlands

    Pieter Van Gorp

  3. Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Natal van Riel

Authors
  1. Danilo Ferreira de Carvalho
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  2. Uzay Kaymak
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  3. Pieter Van Gorp
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  4. Natal van Riel
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Corresponding author

Correspondence to Danilo Ferreira de Carvalho .

Editor information

Editors and Affiliations

  1. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

  2. University of Oviedo, Oviedo, Spain

    Inés Couso

  3. University of Cádiz, Cádiz, Spain

    Jesús Medina

  4. University of Warsaw, Warsaw, Poland

    Dominik Ślęzak

  5. University of Perugia, Perugia, Italy

    Davide Petturiti

  6. Sorbonne Université, Paris, France

    Bernadette Bouchon-Meunier

  7. Iona College, New Rochelle, NY, USA

    Ronald R. Yager

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de Carvalho, D.F., Kaymak, U., Van Gorp, P., van Riel, N. (2022). Population and Individual Level Meal Response Patterns in Continuous Glucose Data. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1602. Springer, Cham. https://doi.org/10.1007/978-3-031-08974-9_19

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  • DOI: https://doi.org/10.1007/978-3-031-08974-9_19

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  • Online ISBN: 978-3-031-08974-9

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