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

Survival Tree01:19

Survival Tree

126
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
126
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
Relative Risk01:12

Relative Risk

262
Relative risk (RR) is a statistical measure commonly used in epidemiology to compare the likelihood of a particular event occurring between two groups. This metric is important for evaluating the relationship between exposure to a specific risk factor and the probability of a particular outcome. It plays a crucial role in medical research, public health studies, and risk assessment. Relative risk quantifies how much more (or less) likely an event is to occur in an exposed group compared to an...
262
Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT01:25

Imaging Studies for Cardiovascular System VI: Calcium -Scoring CT

50
Calcium-Scoring CT ScanA calcium-scoring CT scan, also known as coronary artery calcium (CAC) scan, detects calcium deposits in the coronary arteries. This test assesses the risk of coronary artery disease (CAD), which can lead to cardiovascular events such as angina, heart failure, and sudden cardiac arrest.A calcium-scoring CT scan is generally recommended for individuals at intermediate risk of CAD without symptoms. It includes:Men aged 40-75 and women aged 50-75: Especially those with a...
50

You might also read

Related Articles

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

Sort by
Same author

Left ventricular recovery and outcomes in patients with a durable left ventricular assist device: the EUROMACS registry.

European heart journal·2026
Same author

Recent advancements in covalent organic frameworks as electrochemical and spectroscopic biosensors.

Chemical communications (Cambridge, England)·2026
Same author

The healthy eating index 2020 and colorectal cancer risk: a prospective study based on the PLCO cohort.

Frontiers in nutrition·2026
Same author

Piperine inhibits NLRP3 inflammasome activation and alleviates inflammatory disease.

Phytomedicine : international journal of phytotherapy and phytopharmacology·2026
Same author

From Single-Ion to Integrated Multi-Ion Platforms: Wearable Sweat Sensors for Electrolyte Monitoring.

Biosensors·2026
Same author

Analysis of clinical characteristics and bronchoalveolar lavage fluid microbial community diversity in non-cystic fibrosis bronchiectasis patients with <i>Pseudomonas aeruginosa</i> colonization.

Microbiology spectrum·2026

Related Experiment Video

Updated: Aug 2, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Tree-Based Risk Factor Identification and Stroke Level Prediction in Stroke Cohort Study.

Junyao Li1, Yuxiang Luo1, Meina Dong1

  • 1School of Mathematics and Statistics, Center for Data Science, Lanzhou University, Lanzhou, 730000, China.

Biomed Research International
|April 20, 2023
PubMed
Summary
This summary is machine-generated.

Identifying key stroke risk factors like hypertension and previous stroke is crucial. Advanced models like XGBoost, combined with SHAP, effectively predict stroke risk and guide diagnosis.

More Related Videos

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K
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

3.0K

Related Experiment Videos

Last Updated: Aug 2, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K
Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.2K
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

3.0K

Area of Science:

  • Medical Informatics
  • Clinical Epidemiology
  • Data Science in Healthcare

Background:

  • Stroke remains a leading cause of disability and mortality worldwide.
  • Accurate identification of stroke risk factors and prediction of stroke severity are critical for effective clinical management.
  • Existing methods for stroke risk assessment may not fully capture the complex interactions between patient characteristics.

Purpose of the Study:

  • To identify significant risk factors for stroke using patient cohort data.
  • To classify stroke levels and evaluate the importance and interactions of patient characteristics.
  • To develop and validate a predictive model for stroke risk assessment.

Main Methods:

  • Utilized cohort data from the Second Hospital of Lanzhou University for analysis.
  • Employed multicategorical classification algorithms, including XGBoost, for stroke level prediction.
  • Applied the Shapley Additive Explanation (SHAP) method to identify positive/negative factor effects and interactions.

Main Results:

  • Hypertension, history of transient ischemia, and prior stroke were identified as the most significant risk factors.
  • Age and gender demonstrated negligible impact on stroke risk in this cohort.
  • The XGBoost model exhibited superior performance in predicting stroke risk, providing a ranked list of contributing factors.

Conclusions:

  • A combined approach using XGBoost and SHAP effectively identifies key stroke risk factors and their interactions.
  • This methodology offers valuable insights for clinical diagnosis and personalized stroke prevention strategies.
  • The study provides a data-driven framework for enhancing stroke risk prediction and management.