Jove
Visualize
Contáctanos
JoVE
x logofacebook logolinkedin logoyoutube logo
ACERCA DE JoVE
Visión GeneralLiderazgoBlogCentro de Ayuda JoVE
AUTORES
Proceso de PublicaciónConsejo EditorialAlcance y PolíticasRevisión por ParesPreguntas FrecuentesEnviar
BIBLIOTECARIOS
TestimoniosSuscripcionesAccesoRecursosConsejo Asesor de BibliotecasPreguntas Frecuentes
INVESTIGACIÓN
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchivo
EDUCACIÓN
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualCentro de Recursos para ProfesoresSitio de Profesores
Términos y Condiciones de Uso
Política de Privacidad
Políticas

Videos de Conceptos Relacionados

Fast Decoupled and DC Powerflow01:24

Fast Decoupled and DC Powerflow

724
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
724
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

238
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
238
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

328
Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
328
Modeling with Differential Equations01:25

Modeling with Differential Equations

3
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
3
The Power Flow Problem and Solution01:26

The Power Flow Problem and Solution

829
Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the power flow program computes...
829
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

519
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
519

También podría leer

Artículos Relacionados

Artículos vinculados a este trabajo por autores compartidos, revista y gráfico de citas.

Ordenar por
Same author

Identifying a Refractory Shock Phenotype in Pediatric Sepsis Using a Vasoactive-Adjusted Shock Index.

Shock (Augusta, Ga.)·2026
Same author

Artificial Intelligence and De-Escalation of Critical Care.

JAMA pediatrics·2026
Same author

Association of Albumin Infusion With Differential Response in Pediatric Sepsis and Septic Shock: Retrospective Analysis Using a U.S. Multicenter 2012-2018 Dataset.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies·2026
Same author

Unplanned Regionalization and Interstate Dependence in Pediatric Hospital Care.

JAMA health forum·2026
Same author

A weakly supervised transformer for rare disease diagnosis and subphenotyping from EHRs with pulmonary case studies.

NPJ digital medicine·2026
Same author

AI in Critical Care-Use for De-Escalation Rather Than Escalation of Care.

JAMA pediatrics·2026

Video Experimental Relacionado

Updated: Jan 13, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.6K

Optimización de la gestión de recursos de la UCIP: un enfoque de simulación de eventos discretos basado en datos para

Alireza Akhondi-Asl1,2,3, Michael L McManus1,2,3, Peter C Laussen4

  • 1Division of Critical Care Medicine, Department of Anesthesiology, Critical Care & Pain Medicine, Boston Children's Hospital, Boston, MA.

Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies
|January 6, 2026
PubMed
Resumen

Este estudio desarrolló un modelo de simulación de eventos discretos (DES) para optimizar el flujo de pacientes y el uso de recursos del hospital. El modelo predijo con precisión los resultados al tener en cuenta la capacidad de la unidad posterior, lo que es crucial para las operaciones hospitalarias eficaces.

Palabras clave:
planificación de la capacidadsimulación de eventos discretosasignación de recursos hospitalariosgestión de operacionesflujo de pacientes

Más Videos Relacionados

Author Spotlight: Evaluating Clinicians' Adoption of Ultrasound-Guided Vascular Cannulation Through Simulation Training
05:04

Author Spotlight: Evaluating Clinicians' Adoption of Ultrasound-Guided Vascular Cannulation Through Simulation Training

Published on: August 9, 2024

1.4K
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

7.0K

Videos de Experimentos Relacionados

Last Updated: Jan 13, 2026

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
09:04

A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump

Published on: June 1, 2022

3.6K
Author Spotlight: Evaluating Clinicians' Adoption of Ultrasound-Guided Vascular Cannulation Through Simulation Training
05:04

Author Spotlight: Evaluating Clinicians' Adoption of Ultrasound-Guided Vascular Cannulation Through Simulation Training

Published on: August 9, 2024

1.4K
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

7.0K

Área de la Ciencia:

  • Investigación de Operaciones en Salud
  • Informática Biomédica
  • Ingeniería de Sistemas

Sus antecedentes:

  • Los hospitales requieren herramientas avanzadas para optimizar la utilización de recursos y el flujo de pacientes.
  • Los conocimientos basados en datos son esenciales para mejorar la gestión hospitalaria.
  • Los modelos existentes pueden no capturar completamente las complejidades del flujo de pacientes en múltiples unidades.

Objetivo del estudio:

  • Desarrollar y evaluar un modelo de simulación de eventos discretos (DES) flexible y basado en datos.
  • Optimizar la utilización de la capacidad y el flujo de pacientes en un sistema hospitalario multiunidad, centrándose en la Unidad de Cuidados Intensivos Pediátricos (UCIP) y las unidades posteriores.
  • Proporcionar una herramienta para mejorar la toma de decisiones operativas del hospital.

Principales métodos:

  • Estudio de modelado y validación de simulación de eventos discretos retrospectivos.
  • Se utilizaron datos históricos de admisión de pacientes de un hospital de referencia cuaternario (Boston Children's Hospital, enero de 2012 - febrero de 2025).
  • Se validó el modelo con un escenario de expansión de la UCIP del mundo real.

Principales resultados:

  • El modelo DES predijo con precisión la estancia hospitalaria y la utilización de la capacidad de la UCIP después de la expansión cuando se incorporaron las capacidades de las unidades posteriores.
  • Las simulaciones destacaron el impacto crítico de los cuellos de botella posteriores en el flujo general de pacientes y la utilización de recursos.
  • El modelo demostró su utilidad para la planificación de la capacidad y la optimización de la programación de nuevas líneas de servicio utilizando datos sintéticos.

Conclusiones:

  • El modelo DES de código abierto simula eficazmente el flujo de pacientes en múltiples unidades hospitalarias.
  • Ofrece una herramienta potente y flexible para que los administradores optimicen las operaciones hospitalarias y la asignación de recursos.
  • El modelo es transferible y adaptable a diversos sistemas y escenarios sanitarios.