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Prediction of Cardiac Arrest in Intensive Care Patients Through Machine Learning

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Precision Medicine Powered by pHealth and Connected Health (ICBHI 2017)

Part of the book series: IFMBE Proceedings ((IFMBE,volume 66))

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Abstract

Cardiac arrest is a critical health condition characterized by absence of traceable heart rate, patient’s loss of consciousness as well as apnea, with inhospital mortality of ~80%. Accurate estimation of patients at high risk is crucial to improve not only the survival rate, but also the quality of life as patients who survived from cardiac arrest have severe neurological effects. Existing research has focused on demonstrating static risk scores without taking account patient’s physiological condition. In this study, we are implementing an integrated model of sequential contrast patterns using Multichannel Hidden Markov Model. These models can capture relations between exposure and control group and offer high specificity results, with an average sensitivity of 78%, and have the ability to identify patients in high risk.

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Author information

Authors and Affiliations

  1. Lab of Computing Medical Informatics and Biomedical Imaging Technologies, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece

    E. Akrivos, N. Maglaveras & I. Chouvarda

  2. Department of Internal Medicine, 424 Military Hospital of Thessaloniki, Thessaloniki, Greece

    E. Akrivos

  3. Alexandroupolis University Hospital/Intensive Care Unit, Alexandroupoli, Greece

    V. Papaioannou

  4. McCormick School of Engineering & Applied Sciences, Department of Electrical Engineering & Computer Science, Northwestern University, Evaston, IL, 60201, USA

    N. Maglaveras

Authors
  1. E. Akrivos
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  2. V. Papaioannou
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  3. N. Maglaveras
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  4. I. Chouvarda
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Corresponding author

Correspondence to E. Akrivos .

Editor information

Editors and Affiliations

  1. Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Nicos Maglaveras

  2. Department of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece

    Ioanna Chouvarda

  3. Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal

    Paulo de Carvalho

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The authors declared no potential conflicts of interest with respect to the authorship and/or publication of this article.

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Akrivos, E., Papaioannou, V., Maglaveras, N., Chouvarda, I. (2018). Prediction of Cardiac Arrest in Intensive Care Patients Through Machine Learning. In: Maglaveras, N., Chouvarda, I., de Carvalho, P. (eds) Precision Medicine Powered by pHealth and Connected Health. ICBHI 2017. IFMBE Proceedings, vol 66. Springer, Singapore. https://doi.org/10.1007/978-981-10-7419-6_5

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  • DOI: https://doi.org/10.1007/978-981-10-7419-6_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7418-9

  • Online ISBN: 978-981-10-7419-6

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