Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

Receiver Operating Characteristic Plot

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...

Residuals and Least-Squares Property

Residuals and Least-Squares Property

The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...

Goodness-of-Fit Test

Goodness-of-Fit Test

The goodness-of-fit test is a type of hypothesis test which determines whether the data "fits" a particular distribution. For example, one may suspect that some anonymous data may fit a binomial distribution. A chi-square test (meaning the distribution for the hypothesis test is chi-square) can be used to determine if there is a fit. The null and alternative hypotheses may be written in sentences or stated as equations or inequalities. The test statistic for a goodness-of-fit test is given as...

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

You might also read

Related Articles

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

Sort by

Same author

Tranexamic acid is associated with thrombosis and reoperation of vascular reconstructions for lower extremity arterial injuries.

Journal of vascular surgery·2026

Same author

Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.

Circulation. Population health and outcomes·2026

Same author

Midterm outcomes from a 20-year experience treating functional popliteal artery entrapment syndrome in athletes.

Journal of vascular surgery·2025

Same author

Hospital setting of endovascular repair influences procedural outcomes in blunt traumatic aortic injury.

Journal of vascular surgery·2025

Same author

Practical Guide to Image-Based Big Data Research.

JAMA surgery·2025

Same author

Medicare Patients with Symptomatic Carotid Disease Requiring Carotid Revascularization are Likely to Have Delayed Access: An Analysis of a Multicenter Surgical Data.

Annals of vascular surgery·2024

Same journal

Comparative outcomes of biologic versus synthetic mesh in inguinal hernia repair: A systematic review and meta-analysis.

Surgery·2026

Same journal

When survival models fail: An interpretable anomaly-detection approach for high-risk phenotypes in resected solid pseudopapillary tumors.

Surgery·2026

Same journal

Familiar but unprepared: Artificial intelligence training needs in graduate medical education.

Surgery·2026

Same journal

One-year health care expenditures and patient out-of-pocket spending after open versus minimally invasive hepatopancreatobiliary surgery.

Surgery·2026

Same journal

Shock index, hypotension, and blood product transfusion as predictors of post-traumatic stress disorder in firearm-related trauma.

Surgery·2026

Same journal

Outcomes following endovascular aortic aneurysm repair in nonagenarian patients.

Surgery·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 24, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Evaluating prediction model performance.

John H Cabot¹, Elsie Gyang Ross²

¹Department of Surgery, Division of Vascular Surgery, Stanford University School of Medicine, Stanford, CA.

|July 7, 2023

Summary

This summary is machine-generated.

Evaluating clinical prediction models requires understanding key performance metrics beyond ROC curves. This includes confusion matrices, F1 scores, and MSE for better resource allocation and patient care.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Jul 24, 2025

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Machine Learning in Healthcare
Biomedical Data Science
Clinical Informatics

Background:

The increasing use of predictive models in clinical settings necessitates robust evaluation methods.
Traditional performance metrics may not fully capture the nuances of model utility in healthcare.

Purpose of the Study:

To outline essential performance metrics for supervised classification and regression models in clinical data analysis.
To emphasize the importance of comprehensive model evaluation for clinical implementation.

Main Methods:

Discussion of fundamental concepts including confusion matrices and receiver operating characteristic (ROC) curves.
Explanation of metrics such as F1 scores, precision-recall curves, and mean squared error (MSE).

Main Results:

Familiarity with a range of performance metrics is crucial for accurate model assessment.
Beyond area under the ROC curve, other metrics offer deeper insights into model performance.

Conclusions:

Effective evaluation of clinical models ensures optimal resource allocation and enhances patient care delivery.
A nuanced understanding of model performance metrics is vital for successful clinical integration.