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Related Concept Videos

Mesh Analysis01:20

Mesh Analysis

Mesh analysis is a valuable method for simplifying circuit analysis using mesh currents as key circuit variables. Unlike nodal analysis, which focuses on determining unknown voltages, mesh analysis applies Kirchhoff's voltage law (KVL) to find unknown currents within a circuit. This method is particularly convenient in reducing the number of simultaneous equations that need to be solved.
A fundamental concept in mesh analysis is the definition of meshes and mesh currents. A mesh is a closed...
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...
Mesh Analysis with Current Sources01:10

Mesh Analysis with Current Sources

Mesh analysis becomes simpler when analyzing circuits with current sources, whether independent or dependent. The presence of current sources reduces the number of equations required for analysis. Two cases illustrate this:
Current Source in One Mesh: The analysis process is straightforward when a current source is found in only one mesh within the circuit. Mesh currents are assigned as usual, with the mesh containing the current source excluded from the analysis. Kirchhoff's voltage law (KVL)...
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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

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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Related Experiment Video

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Surrogate Model Development for Digital Experiments in Welding
09:17

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Published on: March 28, 2025

Optimal training sets for Bayesian prediction of MeSH assignment.

Sunghwan Sohn1, Won Kim, Donald C Comeau

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institute of Health, Bethesda, MD 20894, USA. sohn@ncbi.nlm.nih.gov

Journal of the American Medical Informatics Association : JAMIA
|April 26, 2008
PubMed
Summary

Optimal training sets significantly improved Medical Subject Headings (MeSH) assignment prediction using the naïve Bayes algorithm, outperforming K-nearest neighbor (KNN) classifiers and offering a feasible approach for complex learning methods.

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Area of Science:

  • Information Science
  • Computer Science
  • Medical Informatics

Background:

  • Medical Subject Headings (MeSH) are crucial for organizing biomedical literature.
  • Accurate MeSH assignment is essential for efficient information retrieval.
  • Traditional methods for training machine learning models can be computationally intensive.

Purpose of the Study:

  • To enhance the prediction accuracy of MeSH assignment using the naïve Bayes algorithm.
  • To investigate the efficacy of optimal training sets derived from an active learning-inspired method.
  • To compare the performance of naïve Bayes with optimal training sets against other classifiers and methods.

Main Methods:

  • Selected 20 MeSH terms across a range of frequencies.
  • Identified optimal training sets comprising documents with and without a specific MeSH term, prioritizing proximity.
  • Employed naïve Bayes, K-nearest neighbor (KNN), and C-modified least squares (CMLS) classifiers for MeSH assignment prediction.
  • Utilized average precision to evaluate classifier performance.

Main Results:

  • Optimal training sets yielded nearly a 200% improvement in MeSH assignment prediction compared to using entire training sets.
  • Naïve Bayes with optimal sets outperformed KNN in 17 out of 20 MeSH assignments.
  • Naïve Bayes with optimal sets demonstrated a 14% average improvement over KNN across all 20 MeSH assignments.
  • Incorporating CMLS with optimal sets provided an additional 6% performance boost.

Conclusions:

  • Smaller, optimal training sets significantly enhance the learning efficiency and predictive performance of the naïve Bayes algorithm for MeSH assignment.
  • The proposed method offers superior performance compared to KNN classifiers.
  • Optimal training sets are compatible with advanced learning methods like CMLS, enabling their application in scenarios where large datasets are infeasible.