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

Longitudinal Research02:20

Longitudinal Research

12.1K
Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
12.1K
Longitudinal Studies01:26

Longitudinal Studies

208
Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
208
Regulation of Stroke Volume01:27

Regulation of Stroke Volume

3.4K
The regulation of stroke volume, which is the amount of blood the heart pumps out during each heartbeat, is critical for maintaining a healthy circulatory system. Stroke volume is influenced by three main factors: preload, contractility, and afterload.
Preload refers to the degree of stretch on the heart before it contracts. It's analogous to the stretching of a rubber band; the more it's stretched, the more forcefully it snaps back. This concept is encapsulated in the Frank-Starling law of the...
3.4K
Cardiac Output II: Effect of Stroke Volume on Cardiac Output01:22

Cardiac Output II: Effect of Stroke Volume on Cardiac Output

1.3K
Cardiac output (CO), the amount of blood the heart pumps per minute, is a parameter in cardiovascular physiology determined by stroke volume and heart rate. Stroke volume, the amount of blood pushed from one of the ventricles per heartbeat, is influenced by preload, afterload, and contractility.
Preload
Preload refers to the initial elongation of the cardiac myocytes before contraction and is related to the volume of blood filling the heart at the end of diastole, or end-diastolic volume. The...
1.3K

You might also read

Related Articles

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

Sort by
Same author

Dual-Functional Metal Interlayer Enables High-Quality GaN Epitaxy and Low-Damage Transfer Towards Flexible Optoelectronics.

Small methods·2026
Same author

High encoding-sensitivity vision sensor with complementary nonlinear neuromorphic computing.

Nature communications·2026
Same author

Efficacy of Pimpinella anisum L. in Menopausal Women with Psychological Symptoms: A Randomized Controlled Study Integrated with Machine Learning Analysis.

Current pharmaceutical design·2026
Same author

NeuroCLIP: A Multimodal Contrastive Learning Method for rTMS-treated Methamphetamine Addiction Analysis.

IEEE journal of biomedical and health informatics·2026
Same author

Post-stroke rehabilitative mechanisms in individualized fatigue level-controlled treadmill training in rats.

Neural regeneration research·2026
Same author

EEG-Driven Arm Movement Decoding: Combining Connectivity and Amplitude Features for Enhanced Brain-Computer Interface Performance.

Bioengineering (Basel, Switzerland)·2025

Related Experiment Video

Updated: Aug 22, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.3K

Longitudinal Data to Enhance Dynamic Stroke Risk Prediction.

Wenyao Zheng1,2, Yun-Hsuan Chen1,2, Mohamad Sawan1,2

  • 1CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou 310024, China.

Healthcare (Basel, Switzerland)
|November 11, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic model using longitudinal electronic health records for improved stroke risk prediction. The new approach significantly enhances accuracy compared to traditional static models, aiding in early prevention.

Keywords:
backward joint modellongitudinal datapredictionpreventionstroke

More Related Videos

Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits
05:08

Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits

Published on: March 15, 2024

1.2K
Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients
07:42

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Published on: December 16, 2022

3.1K

Related Experiment Videos

Last Updated: Aug 22, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.3K
Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits
05:08

Gathering Self-Initiated Rat Behavioral Data to Characterize Post-Stroke Deficits

Published on: March 15, 2024

1.2K
Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients
07:42

Dual-Task Stroop Paradigm for Detecting Cognitive Deficits in High-Functioning Stroke Patients

Published on: December 16, 2022

3.1K

Area of Science:

  • Medical Informatics
  • Public Health
  • Biostatistics

Background:

  • Electronic health records (EHRs) are crucial for stroke risk prediction.
  • Existing models often use static, single-time data, limiting dynamic risk assessment.
  • Few studies leverage historical health measurements for dynamic modeling.

Purpose of the Study:

  • To develop and validate a dynamic stroke risk prediction model using longitudinal EHR data.
  • To compare the predictive accuracy of the dynamic model against traditional static models.
  • To identify key longitudinal factors influencing stroke risk.

Main Methods:

  • Application of a backward joint model to longitudinal data from the Chinese Longitudinal Healthy Longevity and Happy Family Study.
  • Analysis of multiple measurements of physiological parameters over time.
  • Comparison with the Cox proportional hazard model.

Main Results:

  • The dynamic model achieved a three-year prediction accuracy of 0.926.
  • This accuracy surpasses the Cox proportional hazard model's accuracy of 0.833.
  • Identified fruit consumption frequency, erythrocyte hematocrit, and glucose as potential stroke-related factors.

Conclusions:

  • Longitudinal data significantly improves stroke risk prediction accuracy.
  • The developed dynamic model shows promise for real-time stroke risk monitoring and prevention.
  • Dynamic modeling offers a more precise approach to understanding chronic disease progression and risk factors.