Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Simulation and critical care modeling.

Jennifer E Kreke1, Andrew J Schaefer, Mark S Roberts

  • 1Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.

Current Opinion in Critical Care
|September 24, 2004
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

An agent-based model of the effects of limited vaccination on novel respiratory infections.

Journal of clinical epidemiology·2026
Same author

Indiana Adults Who Participated In Treatment Court Programs Had Better Health Outcomes Than Those Who Did Not.

Health affairs (Project Hope)·2025
Same author

Estimated Burden of Influenza and Direct and Indirect Benefits of Influenza Vaccination.

JAMA network open·2025
Same author

Development and validation of a histology-specific natural history model of ovarian cancer.

American journal of obstetrics and gynecology·2025
Same author

Cost-effectiveness of personalized policies for implementing organ-at-risk sparing adaptive radiation therapy in head and neck cancer.

Physics and imaging in radiation oncology·2025
Same author

Externally validated digital decision support tool for time-to-osteoradionecrosis risk-stratification using right-censored multi-institutional observational cohorts.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology·2025
Same journal

Cardiogenic shock - toward phenotype-directed, precision management.

Current opinion in critical care·2026
Same journal

The future of critical care nutrition: from calorie counting to precision personalized metabolism therapy.

Current opinion in critical care·2026
Same journal

Editorial introduction.

Current opinion in critical care·2026
Same journal

Generative artificial intelligence for outcome prediction in critical care: the future is now?

Current opinion in critical care·2026
Same journal

Feeding under support in critical care illness: metabolic and nutritional management during extracorporeal membrane oxygenation and continuous renal replacement therapy.

Current opinion in critical care·2026
Same journal

Multinational collaborations in critical care research: feasible and useful?

Current opinion in critical care·2026
See all related articles

Simulation modeling offers a powerful approach to understanding complex critical care scenarios. These techniques help analyze intricate relationships between diseases, treatments, and patient factors for better decision-making.

Area of Science:

  • Critical care medicine
  • Health systems engineering
  • Computational modeling

Background:

  • Critical care decision-making involves complex interactions between diseases, interventions, and patient characteristics.
  • Traditional methods like decision trees and statistical modeling struggle with the increasing complexity of critical care problems.

Purpose of the Study:

  • To review simulation modeling techniques applicable to critical care.
  • To demonstrate the utility of these models in analyzing complex critical care systems.

Main Methods:

  • Review of simulation modeling techniques including Markov modeling, Monte Carlo simulation, and discrete-event simulation.
  • Presentation of literature examples showcasing the application of these techniques in critical care.

Related Experiment Videos

Main Results:

  • Simulation models are effective tools for analyzing complex systems in critical care.
  • Examples illustrate the practical application of Markov modeling, Monte Carlo simulation, and discrete-event simulation in addressing real-world critical care challenges.

Conclusions:

  • Simulation models aid in organizing and analyzing the interplay of therapies, tradeoffs, and patient outcomes.
  • These models provide valuable insights for improving critical care management and decision-making.