Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Two-Compartment Open Model: IV Infusion01:15

Two-Compartment Open Model: IV Infusion

291
A two-compartment model is a vital tool in pharmacokinetics, providing an essential understanding of drug behavior, especially for those administered via zero-order intravenous infusion. This model outlines two compartments: the central compartment, where elimination occurs, and the peripheral compartment.
The model illustrates the decrease in plasma drug concentration from the central compartment with a specific equation. It shows that under steady-state conditions, the drug's input rate...
291
One-Compartment Open Model for IV Bolus Administration: Estimation of Clearance00:56

One-Compartment Open Model for IV Bolus Administration: Estimation of Clearance

117
Clearance is a key pharmacokinetic parameter that quantifies the volume of body fluid from which a drug is entirely removed within a specific time frame. It is crucial in assessing how a drug is eliminated from the body and has critical clinical applications.
In the one-compartment open model for intravenous (IV) bolus administration, clearance is estimated by dividing the elimination rate by the plasma drug concentration. This equation leverages the elimination rate constant and the apparent...
117
Clearance Models: Compartment Models01:25

Clearance Models: Compartment Models

109
Clearance measures drug elimination from the central compartment, including plasma and highly perfused organs like kidneys and liver. Its calculation varies depending on pharmacokinetic models and administration routes. The one-compartment model, for instance, portrays the pharmacokinetics of polar drugs such as aminoglycoside antibiotics administered intravenously and readily excreted in urine. In this case, clearance is influenced by the terminal rate constant (λz) and the total volume...
109
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

115
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.
115
One-Compartment Model: IV Infusion01:09

One-Compartment Model: IV Infusion

247
Intravenous (IV) infusion is often utilized when continuous and controlled drug delivery is necessary, such as during surgery or in the treatment of chronic diseases. This method offers numerous advantages, including immediate drug action, precise control over dosage, and bypassing the first-pass metabolism.
The one-compartment model for IV infusion uses mathematical equations to describe the rate of change in drug quantity in the body. At steady-state or infusion equilibrium, the drug input...
247
One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution01:09

One-Compartment Open Model for IV Bolus Administration: Estimation of Elimination Rate Constant, Half-Life and Volume of Distribution

398
The one-compartment open model is a simplified approach used in pharmacokinetics to understand the distribution and elimination of a drug administered through an intravenous bolus. This model assumes rapid drug dispersal throughout the body and elimination using a first-order process. Key pharmacokinetic parameters, such as the elimination rate constant (k), half-life (t1/2), and the apparent volume of distribution (Vd), can be estimated from this model. The elimination rate is calculated...
398

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Hybrid deep learning ensemble model for detecting small to medium rotator cuff tears from shoulder radiographs.

Journal of orthopaedic surgery and research·2026
Same author

From Fitness to Cognition: Machine-Learning Prediction of Cognitive Performance Using Physiological Parameters in Healthy Adults.

Medicine and science in sports and exercise·2026
Same author

The prescribing patterns and effectiveness of sedatives and analgesics for severe traumatic brain injury patients in Taiwan.

Journal of critical care·2025
Same author

Negative prognostic factors and clinical improvement prediction modeling for extracorporeal shockwave therapy in calcific shoulder tendinitis using artificial intelligence techniques.

Journal of shoulder and elbow surgery·2025
Same author

Screening prediction models using artificial intelligence for moderate-to-severe obstructive sleep apnea in patients with acute ischemic stroke.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association·2024
Same author

Research on surface roughness of high-speed milling 7075-T6 aluminum alloy using nanofluid/ultrasonic atomization minimal quantity lubrication system.

Science progress·2024

Related Experiment Video

Updated: Aug 6, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K

Prediction of vancomycin initial dosage using artificial intelligence models applying ensemble strategy.

Wen-Hsien Ho1,2,3, Tian-Hsiang Huang4, Yenming J Chen5

  • 1Department of Healthcare Administration and Medical Informatics, Kaohsiung Medical University, No. 100, Shin-Chuan 1st Road, Kaohsiung, 807, Taiwan.

BMC Bioinformatics
|March 23, 2023
PubMed
Summary
This summary is machine-generated.

An ensemble strategy model accurately predicts vancomycin treatment suitability, aiding clinical dosing decisions. This approach enhances both efficacy and safety in antibiotic therapy, addressing global antibiotic resistance concerns.

Keywords:
Ensemble strategyMonitoring of blood concentration of drugsTherapeutic drug monitoring (TDM)Vancomycin

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
A Novel Method to Determine the Longitudinal Antibacterial Activity of Drug-Eluting Materials
06:18

A Novel Method to Determine the Longitudinal Antibacterial Activity of Drug-Eluting Materials

Published on: March 3, 2023

1.5K

Related Experiment Videos

Last Updated: Aug 6, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.3K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.9K
A Novel Method to Determine the Longitudinal Antibacterial Activity of Drug-Eluting Materials
06:18

A Novel Method to Determine the Longitudinal Antibacterial Activity of Drug-Eluting Materials

Published on: March 3, 2023

1.5K

Area of Science:

  • Pharmacology and Clinical Pharmacy
  • Artificial Intelligence in Medicine
  • Computational Biology

Background:

  • Antibiotic resistance is a growing global health crisis.
  • Vancomycin, a critical antibiotic, has a narrow therapeutic index, demanding precise dosing.
  • Ensuring both efficacy and safety is paramount for suitable vancomycin treatment regimens.

Purpose of the Study:

  • To develop and validate an "ensemble strategy model" for predicting the suitability of vancomycin regimens.
  • To improve clinical decision-making for vancomycin dosing by considering patient-specific factors.

Main Methods:

  • Utilized a dataset of 2141 patients with vancomycin regimens, including six diagnostic attributes.
  • Employed ensemble learning techniques such as AdaBoost.M1, Bagging, fastAdaboost, Neyman-Pearson, and Stacking.
  • Applied a voting mechanism within the ensemble strategy for final suitability prediction.

Main Results:

  • The ensemble strategy model achieved an average accuracy of 86.51% with high robustness.
  • The model demonstrated a test set accuracy of 87.54%, sensitivity of 89.25%, and specificity of 85.19%.
  • Individual algorithms showed accuracies ranging from 81-86%, highlighting the ensemble's superior performance.

Conclusions:

  • The proposed "ensemble strategy model" is a reliable tool for guiding clinical vancomycin dosing.
  • This model can serve as a valuable reference to optimize vancomycin treatment efficacy and patient safety.