Research paper
Next-generation, personalised, model-based critical care medicine : a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them
About this item
- Title
- Next-generation, personalised, model-based critical care medicine : a state-of-the art review of in silico virtual patient models, methods, and cohorts, and how to validation them
- Content partner
- University of Canterbury Library
- Collection
- UC Research Repository
- Description
© 2018 The Author(s). Critical care, like many healthcare areas, is under a dual assault from significantly increasing demographic and economic pressures. Intensive care unit (ICU) patients are highly variable in response to treatment, and increasingly aging populations mean ICUs are under increasing demand and their cohorts are increasingly ill. Equally, patient expectations are growing, while the economic ability to deliver care to all is declining. Better, more productive care is thus the ...
- Format
- Research paper
- Research format
- Journal article
- Date created
- 2018
- Creator
- Chase, Geoff / Preiser JC / Dickson JL / Pironet A / Chiew YS / Pretty CG / Shaw, Geoff / Benyo B / Moeller K / Safaei S / Tawhai M / Hunter P / Desaive T
- URL
- http://hdl.handle.net/10092/16245
- Related subjects
- Humans / Critical Care / Cohort Studies / Models, Biological / Computer Simulation / Physiological Phenomena / Precision Medicine / Biomedical and clinical sciences / Clinical sciences / Intensive care / Technology / Medical Biotechnology / Information and computing sciences / Artificial intelligence / Modelling and simulation / Information and Computing Sciences / Artificial Intelligence and Image Processing / Virtual Reality and Related Simulation
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© The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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