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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

119
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
119
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

179
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
179
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

207
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...
207
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

132
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.
132
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

111
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...
111
Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

991
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.
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
991

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Deep Neural Networks for Image-Based Dietary Assessment
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Demand forecasting model for time-series pharmaceutical data using shallow and deep neural network model.

R Rathipriya1, Abdul Aziz Abdul Rahman2, S Dhamodharavadhani1

  • 1Department of Computer Science, Periyar University, Salem, India.

Neural Computing & Applications
|October 17, 2022
PubMed
Summary
This summary is machine-generated.

Shallow neural networks provide more accurate pharmaceutical demand forecasting than deep learning models. These findings aid in optimizing sales and marketing strategies for drug products.

Keywords:
Deep learning models, Demand forecastingPharmaceuticalindustryShallow neural network models

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Area of Science:

  • Pharmaceutical Sciences
  • Data Science
  • Machine Learning

Background:

  • Effective demand forecasting is crucial for pharmaceutical companies to succeed globally.
  • Demand Forecast Models (DFMs) help predict future product needs.
  • Understanding sales and marketing strategies relies on accurate forecasting.

Purpose of the Study:

  • To validate shallow and deep neural network methods for pharmaceutical demand forecasting.
  • To recommend sales and marketing strategies based on identified trends and seasonality.
  • To compare the predictive accuracy of different neural network architectures.

Main Methods:

  • Utilized various shallow and deep neural network models for demand forecasting.
  • Analyzed eight distinct groups of pharmaceutical products with varying characteristics.
  • Employed Root Mean Squared Error (RMSE) to evaluate the predictive accuracy of DFMs.

Main Results:

  • The mean RMSE for shallow neural network-based DFMs was 6.27 across all drug categories.
  • Shallow neural network models demonstrated superior predictive accuracy compared to deep neural network models.
  • Identified trend and seasonal effects within different pharmaceutical product groups.

Conclusions:

  • DFMs based on shallow neural networks are effective for estimating future pharmaceutical product demand.
  • Shallow neural networks offer a reliable approach for optimizing pharmaceutical sales and marketing strategies.
  • The study validates the utility of neural networks in pharmaceutical market analysis.