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Related Concept Videos

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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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...
54
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

37
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...
37
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

68
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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Pharmacovigilance01:19

Pharmacovigilance

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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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Updated: May 22, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Optimizing elderly care: A data-driven AI model for predicting polypharmacy risk in the elderly using SHARE data.

Aliaa A Elhosseiny1, Seif Eldawlatly2, Eman Ramadan3

  • 1Institute of Global Health and Human Ecology (I-GHHE), The American University in Cairo, Cairo, Egypt; Department of Pharmacology and Toxicology, Faculty of Pharmacy, The British University in Egypt, Cairo, Egypt.

Neuroscience
|May 7, 2025
PubMed
Summary
This summary is machine-generated.

Polypharmacy (PP) is increasing in older adults. Machine learning models can predict PP risk using longitudinal data, highlighting mental health as a key factor.

Keywords:
AgingLongitudinalMachine LearningPolypharmacyPredictiveSHARE

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Area of Science:

  • Gerontology and Public Health
  • Computational Medicine
  • Health Services Research

Background:

  • Aging populations face multimorbidity, increasing the complexity of healthcare.
  • Polypharmacy (PP), defined as using more than five medications concurrently, is a significant challenge in older adults.
  • PP contributes to declines in cognitive and physical function.

Purpose of the Study:

  • To predict the risk of polypharmacy (PP) in individuals over 50 years old.
  • To analyze PP trends over 2, 4, and 6-year intervals using longitudinal data.
  • To identify key predictors of PP risk.

Main Methods:

  • Utilized data from the SHARE study, focusing on participants over 50 across multiple waves.
  • Employed LASSO regression to select 17 key predictor variables for PP risk.
  • Evaluated eight machine learning (ML) models, including Categorical Boosting, using cross-validation.

Main Results:

  • Polypharmacy prevalence shows an upward trend, increasing from 34.03% to 39.91% across study waves.
  • Identified socio-demographic, lifestyle, physical/mental health, and disease history as key PP predictors.
  • The Categorical Boosting ML model achieved the highest accuracy (up to 75.08%) and recall (up to 72.83%) in predicting PP risk.

Conclusions:

  • Polypharmacy (PP) prevalence is rising among older adults.
  • Longitudinal data combined with machine learning (ML) offers a viable approach for PP risk prediction.
  • Mental health status is a crucial factor to consider in managing and mitigating PP.