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Related Concept Videos

Prediction Intervals01:03

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. 
The...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Multiple Sclerosis l: Introduction01:19

Multiple Sclerosis l: Introduction

Multiple sclerosis is a chronic autoimmune disease of the central nervous system (CNS) that affects the brain, spinal cord, and optic nerves. It is an inflammatory demyelinating disorder and a leading cause of neurological disability in young adults.EpidemiologyMS commonly begins between 20 and 40 years of age and is twice as common in women. Its exact cause remains unclear, but genetic susceptibility contributes, with higher risk in first-degree relatives and identical twins. A greater...
Confidence Intervals01:21

Confidence Intervals

An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
Probability Distributions01:32

Probability Distributions

The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson probability...

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Related Experiment Video

Updated: May 26, 2026

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
08:04

Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

Published on: June 6, 2025

Reliable Uncertainty Under Class Imbalance and Distribution Shift: Class-Conditional Conformal Prediction of Multiple

Alexander S Millar, Cortnee Román, Ramkiran Gouripeddi

    Medrxiv : the Preprint Server for Health Sciences
    |May 25, 2026
    PubMed
    Summary

    Class-conditional conformal prediction (CP) improves uncertainty quantification for rare diseases like multiple sclerosis (MS) in AI diagnostics. This method ensures reliable predictions even with imbalanced data and shifting conditions.

    Related Experiment Videos

    Last Updated: May 26, 2026

    Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons
    08:04

    Identification and Classification of Position-specific GABAA Receptor Subunit Missense Variants for Their Role In Hippocampal Pyramidal Neurons

    Published on: June 6, 2025

    Area of Science:

    • Medical Imaging AI
    • Machine Learning
    • Uncertainty Quantification

    Background:

    • Severe class imbalance and distribution shift challenge AI diagnostic reliability.
    • Accurate uncertainty quantification (UQ) is crucial for clinical AI deployment.
    • Multiple Sclerosis (MS) diagnosis via MRI presents a relevant clinical use case.

    Purpose of the Study:

    • To evaluate class-conditional conformal prediction (CP) for reliable UQ in imbalanced medical datasets.
    • To assess CP performance under distribution shift using MRI-based MS diagnosis.
    • To investigate the impact of calibration set size on UQ.

    Main Methods:

    • Evaluated marginal and class-conditional CP on 720 T2-weighted MRI scans (142 MS, 578 controls).
    • Tested a CNN model under distribution shift (1.5 T vs. 3 T data, synthetic degradations).
    • Conducted 100 Monte Carlo experiments to assess coverage, class performance, and UQ variance.

    Main Results:

    • Marginal CP severely under-covered the MS class (16.9%) compared to controls (95.2%).
    • Class-conditional CP significantly improved MS coverage (77.5% at 1.5 T, 85.8% at 3 T) while maintaining control coverage (>89%).
    • CP demonstrated validity under distribution shift, with prediction set sizes reflecting shift severity.

    Conclusions:

    • Class-conditional CP effectively addresses undercoverage of minority classes in AI diagnostics.
    • The approach provides a practical, model-agnostic UQ solution for clinical AI systems.
    • Understanding variance trade-offs is key for deploying diagnostic AI in diverse clinical settings.