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Interactive Process Indicators for Obesity Modelling Using Process Mining

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Advanced Computational Intelligence in Healthcare-7

Part of the book series: Studies in Computational Intelligence ((SCI,volume 891))

Abstract

World Health Organisation defines overweight and obesity as abnormal or excessive fat accumulation that represents a risk to health. Obesity and overweight are associated with increased risk of comorbidities and social problems that negatively impact quality of life. Due to the complexity of the problems, it is necessary to classify obesity based on a set of factors rather than a simple increase in body weight. The objectives of this work were to examine BMI and data available from comorbidities associated to obesity, from a dynamic perspective thanks to the use of process mining tools, in order to obtain patterns of patients’ behaviours. On the other hand, to develop a set of human-readable and contextualised interactive process indicators (iPIs) in the field of obesity, related conditions support health professionals to interact with the process. Modelling iPIs as enhanced views will help the professionals to better perceive of these processes. Professionals will monitor the patient’s progress iteratively and will interact with the system to fine-tune interventions and treatments. The developed strategy can support both the characterisation of general process-based PI and the analysis of individual and personalised aspects of the processes going from general to individual. This method was applied to real data extracted from a tertiary hospital in Spain.

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Authors and Affiliations

  1. Universitat Politècnica de València, Camino de Vera S/N, 46022, Valencia, Spain

    Zoe Valero-Ramon, Carlos Fernandez-Llatas, Antonio Martinez-Millana & Vicente Traver

Authors
  1. Zoe Valero-Ramon
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  2. Carlos Fernandez-Llatas
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  3. Antonio Martinez-Millana
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  4. Vicente Traver
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Corresponding author

Correspondence to Zoe Valero-Ramon .

Editor information

Editors and Affiliations

  1. Department of Digital Systems, University of Piraeus, Piraeus, Greece

    Ilias Maglogiannis

  2. Computer Information Systems, Missouri State University, Springfield, MO, USA

    Sheryl Brahnam

  3. School of Electrical and Information Engineering, University of South Australia, Adelaide, Australia

    Lakhmi C. Jain

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Valero-Ramon, Z., Fernandez-Llatas, C., Martinez-Millana, A., Traver, V. (2020). Interactive Process Indicators for Obesity Modelling Using Process Mining. In: Maglogiannis, I., Brahnam, S., Jain, L. (eds) Advanced Computational Intelligence in Healthcare-7. Studies in Computational Intelligence, vol 891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-61114-2_4

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