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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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.
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...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

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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...
127
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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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.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
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Time-Series Graph00:54

Time-Series Graph

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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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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Pharmacokinetic Models: Overview01:20

Pharmacokinetic Models: Overview

682
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...
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Modelling temporal data in knowledge graphs: a systematic review protocol.

Sepideh Hooshafza1,2, Fabrizio Orlandi2, Rachel Flynn1

  • 1Health Information and Quality Authority (HIQA), Cork, Ireland.

HRB Open Research
|March 4, 2024
PubMed
Summary
This summary is machine-generated.

This systematic review protocol outlines the study of temporal data modeling in knowledge graphs. Findings will inform healthcare data management and analysis of rapidly changing patient information.

Keywords:
Knowledge graphresource description frameworktemporal data

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Area of Science:

  • Computer Science
  • Information Science
  • Healthcare Informatics

Background:

  • High-quality healthcare data is crucial but faces challenges due to high dimensionality and irregularity.
  • Knowledge graphs offer a promising approach for data representation, yet their suitability for healthcare data, especially temporal data, is underexplored.
  • Managing rapidly changing patient data is a significant challenge, as traditional models often fail to account for temporal aspects.

Purpose of the Study:

  • To outline a protocol for an inter-disciplinary systematic review on modeling temporal data in knowledge graphs.
  • To investigate existing approaches, applications, and challenges in temporal data modeling within knowledge graphs.
  • To inform the application of knowledge graphs for effective healthcare data management.

Main Methods:

  • The review focuses on the research question: "What are the existing approaches in modeling temporal data in Resource Description Framework (RDF) based knowledge graphs?"
  • Two sub-questions will evaluate applications and challenges.
  • Searches will be conducted in the ACM digital library, IEEE Xplore, and Scopus, limited to peer-reviewed literature on RDF-based knowledge graphs. A narrative synthesis will be performed.

Main Results:

  • The systematic review will identify and synthesize current approaches to modeling temporal data in RDF-based knowledge graphs.
  • It will highlight key applications where temporal knowledge graphs are utilized.
  • Challenges associated with temporal data modeling in knowledge graphs will be elucidated.

Conclusions:

  • The findings will provide valuable insights for data engineers in representing and analyzing data using temporal data models.
  • The results will be applicable to the healthcare domain, addressing challenges in managing dynamic patient data.
  • This review will facilitate the adoption of knowledge graphs for improved healthcare data management and analytics.