| Title |
Systems modeling and simulation applications for critical care medicine
|
|---|---|
| Published in |
Annals of Intensive Care, June 2012
|
| DOI | 10.1186/2110-5820-2-18 |
| Pubmed ID | |
| Authors |
Yue Dong, Nicolas W Chbat, Ashish Gupta, Mirsad Hadzikadic, Ognjen Gajic |
| Abstract |
Critical care delivery is a complex, expensive, error prone, medical specialty and remains the focal point of major improvement efforts in healthcare delivery. Various modeling and simulation techniques offer unique opportunities to better understand the interactions between clinical physiology and care delivery. The novel insights gained from the systems perspective can then be used to develop and test new treatment strategies and make critical care delivery more efficient and effective. However, modeling and simulation applications in critical care remain underutilized. This article provides an overview of major computer-based simulation techniques as applied to critical care medicine. We provide three application examples of different simulation techniques, including a) pathophysiological model of acute lung injury, b) process modeling of critical care delivery, and c) an agent-based model to study interaction between pathophysiology and healthcare delivery. Finally, we identify certain challenges to, and opportunities for, future research in the area. |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 3% |
| United Kingdom | 2 | 2% |
| France | 1 | <1% |
| Ireland | 1 | <1% |
| Germany | 1 | <1% |
| Brazil | 1 | <1% |
| Spain | 1 | <1% |
| Canada | 1 | <1% |
| Unknown | 107 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 21 | 18% |
| Other | 15 | 13% |
| Researcher | 15 | 13% |
| Student > Master | 15 | 13% |
| Student > Bachelor | 8 | 7% |
| Other | 27 | 23% |
| Unknown | 17 | 14% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 34 | 29% |
| Engineering | 19 | 16% |
| Computer Science | 16 | 14% |
| Business, Management and Accounting | 6 | 5% |
| Psychology | 4 | 3% |
| Other | 18 | 15% |
| Unknown | 21 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 17 July 2014.
All research outputs
#14,146,599
of 22,668,244 outputs
Outputs from Annals of Intensive Care
#745
of 1,033 outputs
Outputs of similar age
#97,481
of 166,052 outputs
Outputs of similar age from Annals of Intensive Care
#10
of 17 outputs
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We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.