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 Concept Videos

Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

390
Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
390
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

658
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
658
The Buckingham Pi Theorem01:09

The Buckingham Pi Theorem

578
The Buckingham Pi theorem provides a structured method to simplify fluid dynamics problems by reducing complex systems of variables to dimensionless terms.
578
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

33
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
33
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

491
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
491

You might also read

Related Articles

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

Sort by
Same author

Uncertainty-aware classification and triage of structural heart disease using electrocardiography and echocardiography metrics.

ArXiv·2026
Same author

Heart-Lung Interactions in Pulmonary Hypertension due to Heart Failure With Preserved Ejection Fraction.

Comprehensive Physiology·2026
Same author

Multiscale computational modeling of the cardiopulmonary consequences of postnatal hyperoxia with implications for preterm-born children.

Biomechanics and modeling in mechanobiology·2026
Same author

Cardiomyocyte NLRP3 signaling in right heart failure is sexually dimorphic via estrogen receptor α.

bioRxiv : the preprint server for biology·2026
Same author

Patient-Specific Lumped-Parameter Model for Quantifying Vessel-Specific Remodeling and Predicting Right Ventricular Function in Pulmonary Hypertension.

Comprehensive Physiology·2026
Same author

Multiscale Computational Modeling of the Cardiopulmonary Consequences of Postnatal Hyperoxia with Implications for Preterm Born Children.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Jun 16, 2025

Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom
06:26

Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom

Published on: February 25, 2022

4.3K

Bayesian parameter inference and uncertainty quantification for a computational pulmonary hemodynamics model using

Amirreza Kachabi1, Sofia Altieri Correa1, Naomi C Chesler1

  • 1Edwards Lifesciences Foundation Cardiovascular Innovation and Research Center, Department of Biomedical Engineering, University of California, Irvine, CA, USA.

Computers in Biology and Medicine
|June 13, 2025
PubMed
Summary

This study developed a computational model for chronic thromboembolic pulmonary hypertension (CTEPH) that efficiently assesses microvascular disease severity. The model

Keywords:
CTEPHHemodynamicsOne-dimensional fluid mechanicsStatistical emulationUncertainty quantification

More Related Videos

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

6.4K
Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.3K

Related Experiment Videos

Last Updated: Jun 16, 2025

Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom
06:26

Particle Image Velocimetry Investigation of Hemodynamics via Aortic Phantom

Published on: February 25, 2022

4.3K
Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
09:20

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction

Published on: February 13, 2021

6.4K
Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.3K

Area of Science:

  • Cardiovascular Research
  • Computational Fluid Dynamics
  • Pulmonary Hypertension Modeling

Background:

  • Clinical diagnostics for pulmonary hypertension have limitations.
  • Subject-specific models offer insights but require computational efficiency and uncertainty quantification.
  • Chronic thromboembolic pulmonary hypertension (CTEPH) can persist after surgery due to microvascular disease.

Purpose of the Study:

  • To develop a computationally efficient, uncertainty-aware 1D fluid dynamics model for assessing microvascular disease in CTEPH.
  • To model individual lungs separately to capture CTEPH heterogeneity.
  • To correlate model-derived microvascular parameters with clinical markers of disease severity.

Main Methods:

  • Utilized a 1D fluid dynamics model with experimental data from a dog model of CTEPH.
  • Incorporated Gaussian process (GP) emulators for accelerated model calibration and uncertainty estimation.
  • Modeled each lung separately to analyze lung-specific microvascular narrowing and resistance.

Main Results:

  • CTEPH induces heterogeneous microvascular adaptation with distinct parameter shifts.
  • Inferred model parameters strongly correlated with clinical markers of disease severity.
  • The model successfully estimated microvascular parameters and their uncertainties within a clinically feasible timeframe.

Conclusions:

  • The developed framework offers a rapid, uncertainty-aware method for evaluating microvascular dysfunction in CTEPH.
  • This approach can support more targeted treatment strategies for persistent pulmonary hypertension.
  • Subject-specific modeling with GP emulators enhances clinical applicability for cardiovascular research.