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Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning

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Biomedical Engineering Systems and Technologies (BIOSTEC 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1400))

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Abstract

The internet of medical things (IoMT) is a relatively new territory for the internet of things (IoT) platforms where we can obtain a significant amount of potential benefits with the combination of cognitive computing. Effective utilization of the healthcare data is the critical factor in achieving such potential, which can be a significant challenge as the medical data is extraordinarily heterogeneous and spread across different devices with different degrees of importance and authority. To address this issue, in this paper, we introduce a cognitive internet of medical things architecture with a use case of early sepsis detection using electronic health records. We discuss the various aspects of IoMT architecture. Based on the discussion, we posit that the proposed architecture could improve the overall performance and usability in the IoMT platforms in particular for different IoMT based services and applications. The use of an RNN-LSTM network for early prediction of sepsis according to Sepsis-3 criteria is evaluated with the empirical investigation using six different time window sizes. The best result is obtained from a model using a four-hour window with the assumption that data is missing-not-at-random. It is observed that when learning from heterogeneous sequences of sparse medical data for early prediction of sepsis, the size of the time window has a considerable impact on predictive performance.

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Authors and Affiliations

  1. Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden

    Mahbub Ul Alam & Rahim Rahmani

Authors
  1. Mahbub Ul Alam
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  2. Rahim Rahmani
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Corresponding author

Correspondence to Mahbub Ul Alam .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xuesong Ye

  2. Fraunhofer Portugal Research Center for Assistive Information and Communication Solutions, Porto, Portugal

    Filipe Soares

  3. Nice Sophia Antipolis University, Nice Cedex 2, France

    Elisabetta De Maria

  4. Universidad Politécnica de Madrid, Madrid, Spain

    Pedro Gómez Vilda

  5. University of Milano-Bicocca, Milan, Italy

    Federico Cabitza

  6. Instituto de Telecomunicações, University of Lisbon, Lisbon, Portugal

    Ana Fred

  7. Universidade Nova de Lisboa, Caparica, Portugal

    Hugo Gamboa

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Alam, M.U., Rahmani, R. (2021). Cognitive Internet of Medical Things Architecture for Decision Support Tool to Detect Early Sepsis Using Deep Learning. In: Ye, X., et al. Biomedical Engineering Systems and Technologies. BIOSTEC 2020. Communications in Computer and Information Science, vol 1400. Springer, Cham. https://doi.org/10.1007/978-3-030-72379-8_18

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  • DOI: https://doi.org/10.1007/978-3-030-72379-8_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72378-1

  • Online ISBN: 978-3-030-72379-8

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