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Modeling and Similitude01:12

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Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
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Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
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Model-driven engineering for digital twins: a graph model-based patient simulation application.

William Trevena1, Xiang Zhong1, Amos Lal2

  • 1Department of Industrial and Systems Engineering, University of Florida, Gainesville, FL, United States.

Frontiers in Physiology
|August 27, 2024
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Summary
This summary is machine-generated.

Digital twins create virtual patient replicas for safe clinical testing. A new scalable architecture enables sepsis patient simulation for training and decision-making.

Keywords:
critical caredigital twinfull-stack application architecturegraph modelvirtual patient simulation

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Area of Science:

  • Biomedical Informatics
  • Computational Medicine
  • Health Informatics

Background:

  • Digital twins offer a risk-free method for testing clinical interventions using virtual patient models.
  • Advancements in electronic health records and sensor data enhance the potential of digital twins in healthcare.

Purpose of the Study:

  • To present a scalable full-stack architecture for a patient simulation application.
  • To enable simulation of critically ill patients with sepsis for medical training and decision support.

Main Methods:

  • Utilized directed acyclic graphs to model causal pathways of physiological interactions and medication effects in sepsis.
  • Developed a three-component architecture: a frontend application, a cloud-hosted serverless simulation engine, and a graph database.

Main Results:

  • Presented a case study demonstrating the viability of the proposed patient simulation architecture.
  • The architecture supports scalable, graph-based patient simulations.

Conclusions:

  • The patient simulation application can aid in training healthcare professionals.
  • This tool can support clinical decision-making at the bedside.