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AIM in Neonatal and Paediatric Intensive Care

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Artificial Intelligence in Medicine

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Abstract

Infections are one of the leading causes of death in infants and detecting life-threatening events in infants is challenging. Thus, providing effective life-saving interventions in time is essential. As infants’ immune and autonomic control system are under development, signs preceding potentially life-threatening events are subtle. Clinical detection is aided by analysis of biomarkers, which unfortunately requires invasive sampling and is time consuming. Infection and inflammation interfere with the autonomic control systems and consequently affect vital signs. Constantly monitoring vital signs at a high frequency enables the immediate detection of discrepancies and is thus a key, noninvasive instrument in modern intensive care units. For pediatric intensive care, several predictive monitoring systems have been developed over the last decade that aim to utilize vital sign monitoring to mitigate the risk of developing life-threatening events, such as sepsis. Recent advances in the field of machine learning have provided novel techniques for big data analysis. This enables an individualized risk assessment via continuous multimodal inputs and development of better clinical decision support systems. These more advanced systems are able to detect sepsis 24 hours earlier than clinical practice and enable an overall risk assessment for future sepsis, life-threatening events, and death at the time of hospitalization or during the first week of life.

This chapter summarizes the current evidence on machine learning-based monitoring systems and provides an overview on the strengths, limitations, and potential future roles of novel machine learning-based methods for the early detection of pedatric sepsis and potentially life-threatening events.

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Authors and Affiliations

  1. Department of Women’s & Children’s Health, Karolinska Institutet, Stockholm, Sweden

    David Forsberg, Antoine Honoré, Kerstin Jost, Emma Persad, Karen Coste, Susanne Rautiainen & Eric Herlenius

  2. Astrid Lindgren Children’s Hospital, Children’s and Women’s Health, Karolinska University Hospital, Stockholm, Sweden

    David Forsberg, Antoine Honoré, Kerstin Jost, Emma Persad, Karen Coste, Susanne Rautiainen & Eric Herlenius

  3. Division of Information Science and Engineering, KTH Royal Institute of Technology, Stockholm, Sweden

    Antoine Honoré & Saikat Chatterjee

  4. Karl Landsteiner University of Health Sciences, Krems, Austria

    Emma Persad

  5. CNRS, INSERM, GReD, Université Clermont Auvergne, Clermont-Ferrand, France

    Karen Coste

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  1. David Forsberg
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  2. Antoine Honoré
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  3. Kerstin Jost
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  4. Emma Persad
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  5. Karen Coste
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  6. Saikat Chatterjee
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  7. Susanne Rautiainen
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  8. Eric Herlenius
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Corresponding author

Correspondence to Eric Herlenius .

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Editors and Affiliations

  1. Karolinska Institute, Stockholm, Sweden

    Niklas Lidströmer

  2. Faculty of Medicine, Imperial College London, London, UK

    Hutan Ashrafian

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Forsberg, D. et al. (2021). AIM in Neonatal and Paediatric Intensive Care. In: Lidströmer, N., Ashrafian, H. (eds) Artificial Intelligence in Medicine. Springer, Cham. https://doi.org/10.1007/978-3-030-58080-3_309-1

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  • DOI: https://doi.org/10.1007/978-3-030-58080-3_309-1

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  • Print ISBN: 978-3-030-58080-3

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