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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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What are Estimates?01:06

What are Estimates?

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It isn't easy to measure a parameter such as the mean height or the mean weight of a population. So, we draw samples from the population and calculate the mean height or mean weight of the individuals in the sample. This sample data acts as a representative measure of the population parameter. These sample statistics are known as estimates. 
The estimate for the mean of a sample is denoted by ͞x, whereas the mean of the population is designated as μ. Further, parameters such...
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Confidence Intervals01:21

Confidence Intervals

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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...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

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A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...
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Estimating Population Mean with Unknown Standard Deviation01:22

Estimating Population Mean with Unknown Standard Deviation

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In practice, we rarely know the population standard deviation. In the past, when the sample size was large, this did not present a problem to statisticians. They used the sample standard deviation s as an estimate for σ and proceeded as before to calculate a confidence interval with close enough results. However, statisticians ran into problems when the sample size was small. A small sample size caused inaccuracies in the confidence interval.
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Related Experiment Video

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Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
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Confidence Estimation via Auxiliary Models.

Charles Corbiere, Nicolas Thome, Antoine Saporta

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |June 4, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for measuring deep learning model confidence using true class probability (TCP), outperforming standard maximum class probability (MCP) for critical applications.

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    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Quantifying deep neural classifier confidence is crucial for safety-critical applications.
    • Standard maximum class probability (MCP) has limitations for accurate confidence estimation.

    Purpose of the Study:

    • Introduce a novel confidence estimation criterion: true class probability (TCP).
    • Develop a method to learn TCP from data using an auxiliary model.
    • Evaluate the proposed approach in failure prediction and domain adaptation tasks.

    Main Methods:

    • Proposed a new target criterion for model confidence: true class probability (TCP).
    • Developed a specific learning scheme to learn TCP from data with an auxiliary model.
    • Evaluated the approach on image classification and semantic segmentation tasks.

    Main Results:

    • True class probability (TCP) demonstrates superior properties for confidence estimation compared to MCP.
    • The proposed approach significantly outperforms strong baselines across various benchmarks.
    • Effective confidence estimates were achieved in failure prediction and domain adaptation.

    Conclusions:

    • The novel TCP criterion and learning scheme provide a more reliable method for quantifying deep learning model confidence.
    • This approach enhances the deployment of deep learning models in safety-critical domains.
    • The method shows broad applicability across different network architectures and dataset sizes.