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

Predicting Molecular Geometry

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries

The Uncertainty Principle

The Uncertainty Principle

Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...

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.

End Point Prediction: Gran Plot

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...

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...

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

Local partisan context and mental health.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same author

Characterization of the cancer-associated field of injury in the nasal epithelium in never-smokers.

Lung cancer (Amsterdam, Netherlands)·2026

Same author

Major Adverse Kidney Events in the American Indian or Alaska Native Population with Diabetes.

Clinical journal of the American Society of Nephrology : CJASN·2026

Same author

WISDOM randomized trial comparing risk-based versus annual breast cancer screening: study cohort characteristics and design.

NPJ breast cancer·2026

Same author

Dynamic Bayesian networks to predict loss of kidney function: a cross-institution use case in a large cohort with or at-risk of CKD.

BMC medical informatics and decision making·2026

Same author

Evaluating the robustness of features generated by a foundation model from CT with different reconstruction parameters.

Proceedings of SPIE--the International Society for Optical Engineering·2026

Search research articles

Related Experiment Video

Updated: Jan 28, 2026

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Published on: February 16, 2024

Evaluating the Impact of Uncertainty on Risk Prediction: Towards More Robust Prediction Models.

Panayiotis Petousis¹, Arash Naeim², Ali Mosleh³

¹Medical Imaging & Informatics, Department of Radiological Sciences and Bioengineering.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|March 1, 2019

Summary

Cancer risk prediction models can be uncertain. This study introduces a framework for uncertainty analysis, providing risk confidence intervals and highlighting how input variability impacts management decisions.

More Related Videos

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Related Experiment Videos

Last Updated: Jan 28, 2026

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Author Spotlight: Advancing Early Detection and Treatment of Gastrointestinal Tumors

Published on: February 16, 2024

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery

Published on: September 27, 2024

Area of Science:

Biostatistics
Epidemiology
Medical Informatics

Background:

Risk prediction models are essential for cancer risk assessment and patient management.
Current models often use multivariate regression, requiring complete data and lacking confidence in predictions.

Purpose of the Study:

To develop and demonstrate a framework for uncertainty analysis in cancer risk prediction.
To quantify the impact of uncertain or missing input values on risk predictions and management decisions.

Main Methods:

Implemented a framework to replace uncertain/missing input values with plausible ranges.
Calculated individualized risk confidence intervals using these ranges.
Applied the framework to the Gail model for breast cancer risk prediction.

Main Results:

Uncertainty analysis revealed that up to 13% of cases had risk intervals crossing decision thresholds.
Missing input values caused a small proportion of cases to shift between low- and high-risk categories.
The study highlights significant variability in risk predictions due to input uncertainty.

Conclusions:

Uncertainty analysis is critical for accurate cancer risk prediction and informed patient management.
Current models may oversimplify risk by not accounting for input variability.
Improved communication of input assumptions is needed to enhance the reliability of risk prediction models.