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Related Concept Videos

Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

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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...
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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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Pharmacokinetic Models: Overview01:20

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Pharmacokinetic models utilize mathematical analysis to achieve a detailed quantitative understanding of a drug's life cycle within the body. They are instrumental in simulating a drug's pharmacokinetic parameters, predicting drug concentrations over time, optimizing dosage regimens, linking concentrations with pharmacologic activity, and estimating potential toxicity.
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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

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Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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How Well Does a Sequential Minimal Optimization Model Perform in Predicting Medicine Prices for Procurement System?

Amarawan Pentrakan1,2, Cheng-Chia Yang1, Wing-Keung Wong3,4,5

  • 1Department of Healthcare Administration, Asia University, Taichung 41354, Taiwan.

International Journal of Environmental Research and Public Health
|June 2, 2021
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Summary

This study developed a predictive algorithm to estimate pharmaceutical prices, improving government budget management. The model achieved 92.62% accuracy, aiding policymakers in setting optimal drug prices.

Keywords:
feature selectionmedicine priceprediction modelsequential minimal optimization

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Area of Science:

  • Health Economics
  • Pharmaceutical Management
  • Computational Science

Background:

  • Pharmaceutical price management poses challenges for government budgets.
  • Thailand's reference price policy faces issues with price dispersion and transparency.
  • An efficient algorithm is needed to estimate medical product prices.

Purpose of the Study:

  • To develop and validate an algorithm for predicting pharmaceutical prices.
  • To improve transparency and efficiency in medicine procurement.
  • To assist health policymakers in price monitoring and setting.

Main Methods:

  • Developed a predictive model using the Sequential Minimal Optimization (SMO) algorithm.
  • Applied feature selection techniques, including gain ratio, to enhance accuracy.
  • Validated the model using a 10-fold cross-validation on 2424 procurement records (Jan-Mar 2019).

Main Results:

  • The SMO algorithm with gain ratio achieved approximately 92.62% accuracy.
  • The model demonstrated high sensitivity and precision in price prediction.
  • Identified eight key features influencing medicine prices: segmented buyers, product groups, trade names, procurement methods, dosage forms, pack sizes, manufacturers, and budgets.

Conclusions:

  • The proposed SMO-based model effectively predicts pharmaceutical prices.
  • The model can inform health policymakers and hospital purchasing managers.
  • Findings support better price monitoring and negotiation in pharmaceutical procurement.