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Related Concept Videos

Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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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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Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

44
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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

56
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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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

387
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.
On...
387
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 Complexity Reduction for ZKML Healthcare Applications: Privacy Protection and Inference Optimization for ZKML

Sathya Krishnasamy1, Ilangovan Govindarajan2

  • 1President and Principal, ChainAim, Newington, Connecticut, USA.

Blockchain in Healthcare Today
|December 9, 2024
PubMed
Summary
This summary is machine-generated.

Web 3.0, utilizing decentralized networks and AI, faces adoption barriers. This paper explores zero-knowledge machine learning (ZKML) as a solution for privacy and efficiency in global healthcare data collection.

Keywords:
ICHOMInternational Consortium for Health Outcomes MeasurementZKMLblockchaindiabetesdistributed ledgermachine learningmodel complexity reduction privacyzero-knowledge machine learning

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Area of Science:

  • Decentralized Systems and Artificial Intelligence
  • Cryptographic Applications in Healthcare

Background:

  • Web 3.0 technologies, including decentralized networks and AI, are advancing but face significant adoption challenges.
  • Previous work identified barriers and mitigations for Web 3.0 adoption in healthcare, focusing on privacy and design optimizations.

Purpose of the Study:

  • To conceptualize the technical and operational feasibility of Zero-Knowledge Machine Learning (ZKML) in global healthcare.
  • To implement a reference healthcare application using ZKML for high-volume data collection and patient-reported outcomes.
  • To advance the use of machine learning models in decentralized healthcare architectures for enhanced data protection and efficiency.

Main Methods:

  • Conceptualization of ZKML's technical and operational feasibility.
  • Implementation of a reference healthcare system using synthetic International Consortium for Health Outcomes Measurement (ICHOM) data.
  • Research on model complexity reduction for the ICHOM diabetes dataset.

Main Results:

  • Demonstrated the conceptual feasibility of ZKML in a global healthcare context.
  • Developed a reference implementation for high-volume data collection, including patient-reported outcomes.
  • Reported model complexity reduction for the ICHOM diabetes dataset, enhancing ML model applicability.

Conclusions:

  • ZKML presents a viable approach to address privacy and inference cost challenges in Web 3.0 healthcare applications.
  • The study provides a foundation for applying ZKML in global healthcare standards, improving data protection and efficiency.
  • Further development is needed to establish baselines for ZKML in widespread global healthcare adoption.