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Related Concept Videos

Pharmacovigilance01:19

Pharmacovigilance

1.0K
Post-marketing surveillance is a critical component of pharmaceutical regulation, often uncovering unanticipated adverse drug reactions (ADRs) once a drug is widely used over an extended period.
This process, termed pharmacovigilance, aims to detect, evaluate, and minimize harmful effects related to medication use. The data collection for pharmacovigilance depends on spontaneous reporting systems, where healthcare professionals or patients voluntarily report suspected ADRs.
In some cases, there...
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Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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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...
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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: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

163
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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

137
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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Using Machine Learning for Pharmacovigilance: A Systematic Review.

Patrick Pilipiec1,2, Marcus Liwicki1, András Bota1

  • 1Embedded Intelligent Systems Lab, Department of Computer Science Electrical and Space Engineering, Luleå University of Technology, 97187 Luleå, Sweden.

Pharmaceutics
|February 26, 2022
PubMed
Summary
This summary is machine-generated.

Natural language processing (NLP) effectively identifies adverse drug reactions from online user content, complementing traditional pharmacovigilance methods. This technology offers a cost-effective and accurate approach to drug safety monitoring.

Keywords:
ADRsadverse drug reactionscomputational linguisticsmachine learningpharmacovigilancepublic healthuser-generated content

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Area of Science:

  • Pharmacovigilance and Computational Linguistics

Background:

  • Traditional pharmacovigilance methods for monitoring adverse drug reactions are often resource-intensive.
  • User-generated content presents a potential, underutilized data source for drug safety.

Purpose of the Study:

  • To systematically review the application of natural language processing (NLP) in analyzing user-generated text for pharmacovigilance.
  • To assess the efficacy of NLP in identifying adverse drug reactions from online textual data.

Main Methods:

  • A comprehensive, multi-disciplinary literature search across four databases was performed.
  • Included studies focused on NLP applications for pharmacovigilance using user-generated content.
  • 16 relevant publications were selected and assessed for reliability and validity.

Main Results:

  • 14 out of 16 studies reported successful identification of adverse drug reactions using NLP on internet-published user-generated content.
  • Consistent positive findings across various drug types indicate NLP's accuracy and effectiveness.
  • All reviewed studies demonstrated medium reliability and validity.

Conclusions:

  • Natural language processing is a viable and accurate tool for identifying adverse drug reactions from online user-generated text.
  • NLP analysis of textual data can significantly supplement traditional pharmacovigilance systems.
  • This approach offers a promising avenue for enhanced drug safety surveillance.