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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Model Approaches for Pharmacokinetic Data: Compartment Models01:14

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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.
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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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.
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Overview of Advanced Functional Groups02:22

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Functional groups are groups of atoms with specific chemical properties that occur within organic molecules and are sometimes denoted as “R”. Functional groups can “functionalize” a compound by enabling it to adopt different physical and chemical properties.
Types of Advanced Functional Groups
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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.
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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Data Modeling Challenges of Advanced Interoperability.

Bernd Blobel1, Frank Oemig2, Pekka Ruotsalainen3

  • 1Medical Faculty, University of Regensburg, Germany.

Studies in Health Technology and Informatics
|April 22, 2018
PubMed
Summary
This summary is machine-generated.

Advanced interoperability solutions are crucial for progressive health systems, facing unique modeling challenges. This study evaluates data models and enterprise architectures, offering amendments to enhance health system modeling and interoperability.

Keywords:
Healthcare transformationarchitecturesdata modelsinteroperabilityknowledge management

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Area of Science:

  • Health Informatics
  • Systems Engineering
  • Information Management

Background:

  • Progressive health paradigms necessitate interdisciplinary approaches and integrated policy domains.
  • Modeling complex health systems presents significant interoperability challenges.

Purpose of the Study:

  • To evaluate existing data models and enterprise business architectures for health system interoperability.
  • To identify limitations in current standards and propose amendments for improved health system modeling.

Main Methods:

  • Classification of data models and enterprise business architectures.
  • Comparison with the ISO Reference Architecture.
  • Evaluation of existing definitions, specifications, and standards for data model interoperability.

Main Results:

  • Existing data models and enterprise architectures have limitations in meeting advanced interoperability needs.
  • The ISO Reference Architecture provides a basis for comparison but requires adaptation.
  • Specific amendments are proposed to enhance the usability of data models.

Conclusions:

  • Addressing health system modeling challenges requires advanced interoperability solutions.
  • Current standards for data models need refinement to support complex, interdisciplinary health paradigms.
  • The proposed amendments aim to improve the effectiveness of data models in progressive health systems.