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Related Concept Videos

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

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

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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

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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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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Dynamic Clamp Methods to Investigate Impaired Neuronal Excitability Associated with Autism
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Methods to window data to differentiate between Markov models.

Jason M Schwier1, Richard R Brooks, Christopher Griffin

  • 1Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC 29634, USA. jschwie@clemson.edu

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|October 7, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces optimal window sizes for detecting changes in serial Markovian data streams. It balances promptness and accuracy, improving behavior change detection in dynamic systems.

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Area of Science:

  • Computer Science
  • Data Science
  • Statistics

Background:

  • Traditional methods for detecting serial Markovian patterns often use maximum likelihood over entire data streams.
  • Detecting rapid behavior changes in data streams is challenging due to trade-offs in sliding window sizes.
  • Hidden Markov Models (HMMs) are commonly used but can be slow to adapt to new behaviors.

Purpose of the Study:

  • To develop methods for reliably and promptly detecting behavior changes in serial Markovian data streams.
  • To establish necessary and sufficient bounds for sliding window sizes in change-point detection.
  • To provide practical approaches for calculating optimal window sizes based on Markov model structures.

Main Methods:

  • Utilizing statistical pattern matching on a sliding window of data samples to detect behavior changes.
  • Analyzing the trade-offs between window size, false-positive rates, and detection delay.
  • Developing two distinct methods for calculating optimal window sizes informed by Markov model state and transition properties.

Main Results:

  • Identified necessary and sufficient bounds for sliding window sizes to optimize change-point detection.
  • Demonstrated that window size directly impacts the balance between detection speed and accuracy.
  • Validated the proposed window size calculation methods through simulations and a real-world consumer purchase data example.

Conclusions:

  • The proposed methods offer a robust framework for timely and accurate detection of behavior shifts in serial Markovian data.
  • Optimal window size selection is critical for effective change-point detection in dynamic data streams.
  • The findings have broad applicability in areas requiring real-time pattern recognition and anomaly detection.