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Virtual Patients and Virtual Cohorts: A New Way to Think About the Design and Implementation of Personalized ICU Treatments

  • J. G. ChaseEmail author
  • T. Desaive
  • J.-C. Preiser
Part of the Annual Update in Intensive Care and Emergency Medicine book series (AUICEM)

Abstract

Intensive care unit (ICU) patients exhibit complex and highly variable behavior, making them very difficult to manage efficiently and safely. More pragmatically, the cost of intensive care in healthcare systems has dramatically risen over the last decades mostly because of patient ageing. The next generation and challenge for ICU care is thus to personalize and improve care to manage inter- and intra-patient variability and improve cost and productivity. Defeating ‘one size fits all’ protocolized approaches and moving to a ‘one method fits all’ personalized approach could provide the big step forward required to handle the demographic tsunami and rising costs.

Computer models offer one powerful opportunity to personalize care by using clinical data and system identification methods to create a so-called ‘virtual patient’ representing the patient in a particular state. This approach relies on identifying patient-specific parameters that are time varying, capture inter- and intra-patient variability, and are not a function of the therapeutic inputs. Such ‘sensitivities’ are the key to unlocking virtual patients and model-based care. Thus, the approach predefines the type of deterministic physiological models used. These models have a long history in physiological studies, but a much shorter one in clinical studies. However, over the last 10 years, the successful design and implementation of model-based sensors or decision support systems [1, 2] has demonstrated the potential of this approach to provide personalized solutions for ICU patients.

Keywords

Intensive Care Unit Intensive Care Unit Patient Virtual Patient Intensive Care Unit Care System Identification Method 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of CanterburyChristchurchNew Zealand
  2. 2.GIGA – Cardiovascular SciencesUniversity of LiègeLiègeBelgium
  3. 3.Department of Intensive Care, Erasme University HospitalUniversité libre de BruxellesBrusselsBelgium

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