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

State Space Representation01:27

State Space Representation

237
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
237
Transfer Function to State Space01:23

Transfer Function to State Space

297
State-space representation is a powerful tool for simulating physical systems on digital computers, necessitating the conversion of the transfer function into state-space form. Consider an nth-order linear differential equation with constant coefficients, like those encountered in an RLC circuit. The state variables are selected as the output and its n−1 derivatives. Differentiating these variables and substituting them back into the original equation produces the state equations.
In an...
297
State Space to Transfer Function01:21

State Space to Transfer Function

236
The conversion of state-space representation to a transfer function is a fundamental process in system analysis. It provides a method for transitioning from a time-domain description to a frequency-domain representation, which is crucial for simplifying the analysis and design of control systems.
The transformation process begins with the state-space representation, characterized by the state equation and the output equation. These equations are typically represented as:
236
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

101
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
101
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

474
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...
474
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

282
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
282

You might also read

Related Articles

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

Sort by
Same author

Resuscitation in paediatric septic shock using vitamin C and hydrocortisone (RESPOND): The RESPOND randomised controlled trial statistical analysis plan.

Critical care and resuscitation : journal of the Australasian Academy of Critical Care Medicine·2026
Same author

"Stuck" on the Ramp: What Factors Influence Patient Comfort and Satisfaction While Waiting on Emergency Department Ramps? A Qualitative Analysis.

Emergency medicine Australasia : EMA·2026
Same author

Burden of invasive group a streptococcus infection in Australia: a systematic review and meta-analysis.

European journal of clinical microbiology & infectious diseases : official publication of the European Society of Clinical Microbiology·2026
Same author

The Impact of Hospital Bed Occupancy on Patient Flow and Emergency Department Access: A 25-Hospital Cohort Study.

The Medical journal of Australia·2026
Same author

Strategies for Reducing Access Block and Waiting Time for Patients Seeking Emergency Hospital Care: Results of a Ward-Level Discrete Event Simulation at Queensland's Largest Public Hospitals.

The Medical journal of Australia·2026
Same author

The Association Between Access Block And Ambulance Ramping, And The Impact of COVID-19: A Retrospective Observational Cohort Study of 25 Queensland Hospitals.

The Medical journal of Australia·2026

Related Experiment Video

Updated: Jul 19, 2025

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.2K

Forecasting emergency department waiting time using a state space representation.

Kelly Trinh1,2, Andrew Staib3,4, Anton Pak5,6

  • 1Data61, The Commonwealth Scientific and Industrial Research Organisation, Clayton, Victoria, Australia.

Statistics in Medicine
|August 10, 2023
PubMed
Summary
This summary is machine-generated.

Accurate emergency department (ED) waiting time forecasts improve patient experience. New state space models enhance ED wait time prediction accuracy by 10% compared to traditional methods.

Keywords:
Bayesian state space modelMCMCemergency department waiting time

More Related Videos

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.4K
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.0K

Related Experiment Videos

Last Updated: Jul 19, 2025

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide
09:52

Setting Up a Stroke Team Algorithm and Conducting Simulation-based Training in the Emergency Department - A Practical Guide

Published on: January 15, 2017

17.2K
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

20.4K
Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps
11:52

Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

Published on: February 9, 2017

6.0K

Area of Science:

  • Health Services Research
  • Biostatistics
  • Health Informatics

Background:

  • Emergency departments (EDs) increasingly provide waiting time information to manage patient flow and improve experience.
  • Limited research exists on the quality and accuracy of ED waiting time information provided to patients.

Purpose of the Study:

  • To develop and evaluate advanced statistical models for forecasting emergency department (ED) waiting times for low-acuity patients.
  • To enhance the accuracy and informativeness of patient-facing ED waiting time data.

Main Methods:

  • Utilized a Bayesian framework with state space models featuring flexible error structures.
  • Incorporated time-varying and correlated error terms.
  • Treated zero-recorded waiting times as unobserved values to improve model performance.

Main Results:

  • State space models significantly improved ED waiting time forecast accuracy over a rolling average benchmark.
  • The proposed models reduced root mean squared errors by 10% compared to the benchmark.
  • Handling zero waiting times as unobserved improved predictive performance.

Conclusions:

  • Advanced state space models offer superior accuracy for ED waiting time forecasting.
  • Improved waiting time information can empower patients to make better decisions, enhancing their overall ED experience.
  • This approach contributes to better ED demand management and patient satisfaction.