↓ Skip to main content

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

Overview of attention for article published in BioMedical Engineering OnLine, February 2018
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)

Mentioned by

5 tweeters


60 Dimensions

Readers on

110 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
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
Published in
BioMedical Engineering OnLine, February 2018
DOI 10.1186/s12938-018-0455-y
Pubmed ID

J. Geoffrey Chase, Jean-Charles Preiser, Jennifer L. Dickson, Antoine Pironet, Yeong Shiong Chiew, Christopher G. Pretty, Geoffrey M. Shaw, Balazs Benyo, Knut Moeller, Soroush Safaei, Merryn Tawhai, Peter Hunter, Thomas Desaive


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 big challenge. One means to that end is personalised care designed to manage the significant inter- and intra-patient variability that makes the ICU patient difficult. Thus, moving from current "one size fits all" protocolised care to adaptive, model-based "one method fits all" personalised care could deliver the required step change in the quality, and simultaneously the productivity and cost, of care. Computer models of human physiology are a unique tool to personalise care, as they can couple clinical data with mathematical methods to create subject-specific models and virtual patients to design new, personalised and more optimal protocols, as well as to guide care in real-time. They rely on identifying time varying patient-specific parameters in the model that capture inter- and intra-patient variability, the difference between patients and the evolution of patient condition. Properly validated, virtual patients represent the real patients, and can be used in silico to test different protocols or interventions, or in real-time to guide care. Hence, the underlying models and methods create the foundation for next generation care, as well as a tool for safely and rapidly developing personalised treatment protocols over large virtual cohorts using virtual trials. This review examines the models and methods used to create virtual patients. Specifically, it presents the models types and structures used and the data required. It then covers how to validate the resulting virtual patients and trials, and how these virtual trials can help design and optimise clinical trial. Links between these models and higher order, more complex physiome models are also discussed. In each section, it explores the progress reported up to date, especially on core ICU therapies in glycemic, circulatory and mechanical ventilation management, where high cost and frequency of occurrence provide a significant opportunity for model-based methods to have measurable clinical and economic impact. The outcomes are readily generalised to other areas of medical care.

Twitter Demographics

The data shown below were collected from the profiles of 5 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 110 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 110 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 19 17%
Student > Ph. D. Student 18 16%
Student > Bachelor 15 14%
Student > Master 12 11%
Other 9 8%
Other 22 20%
Unknown 15 14%
Readers by discipline Count As %
Engineering 26 24%
Medicine and Dentistry 15 14%
Computer Science 8 7%
Biochemistry, Genetics and Molecular Biology 6 5%
Nursing and Health Professions 5 5%
Other 25 23%
Unknown 25 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 23 May 2020.
All research outputs
of 17,788,816 outputs
Outputs from BioMedical Engineering OnLine
of 715 outputs
Outputs of similar age
of 284,081 outputs
Outputs of similar age from BioMedical Engineering OnLine
of 1 outputs
Altmetric has tracked 17,788,816 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 715 research outputs from this source. They receive a mean Attention Score of 3.5. This one has done well, scoring higher than 81% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 284,081 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them