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

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

64
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...
64
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

81
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...
81
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

114
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
114
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

181
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...
181
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

96
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...
96
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

295
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...
295

You might also read

Related Articles

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

Sort by
Same author

The Rise of Social Media Websites: How Plastic Surgeons Interact with Patients in the Digital Age, a Multicenter Study of China.

Aesthetic plastic surgery·2026
Same author

Cryptanalysis and improvement of a distributed zero trust scheme for airborne wireless sensor networks.

Scientific reports·2026
Same author

Epigallocatechin-3-gallate attenuates the immunotoxicity of sulfamethoxazole and reduces its residues in crayfish via phagocytosis.

Fish & shellfish immunology·2026
Same author

Mechanism of epigallocatechin-3-gallate in alleviating polychlorinated biphenyls-induced immunotoxicity in Scyllaparamamosain.

Environmental pollution (Barking, Essex : 1987)·2026
Same author

Association of gut microbiota and inflammatory markers with enteral nutrition intolerance in patients with early-stage moderate-to-severe intracerebral hemorrhage.

Microbiology spectrum·2026
Same author

Horn-Shaped Perforator Flaps for Plantar.

Journal of clinical medicine·2026

Related Experiment Video

Updated: Jul 19, 2025

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

Machine learning and statistical models for analyzing multilevel patent data.

Sunyun Qi1, Yu Zhang2, Hua Gu1

  • 1Zhejiang Provincial Center for Medical Science Technology and Education Development, Hangzhou, 310000, Zhejiang, China.

Scientific Reports
|August 7, 2023
PubMed
Summary

Public hospitals in China are filing more patents, indicating healthcare innovation. Key factors like health technicians and R&D spending positively influence patent numbers in tertiary hospitals.

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

Related Experiment Videos

Last Updated: Jul 19, 2025

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
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.0K
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.7K

Area of Science:

  • Health Services Research
  • Health Economics
  • Innovation Studies

Background:

  • A notable increase in patent applications from Chinese public hospitals highlights growing healthcare innovation.
  • Measuring national healthcare innovation capacity through patent counts is a critical area of research.
  • Understanding the drivers behind this surge in healthcare patents is essential for policy development.

Purpose of the Study:

  • To investigate the relationship between patent applications and ten independent variables in Chinese tertiary public hospitals.
  • To identify key factors influencing healthcare innovation capacity as reflected by patent filings.
  • To compare the performance of different statistical models for analyzing patent data.

Main Methods:

  • Utilized variable selection and LASSO regression to address multicollinearity.
  • Employed Poisson and negative binomial models for initial patent data analysis.
  • Implemented agglomerative hierarchical clustering, followed by a negative binomial mixed model, validated by likelihood ratio tests.

Main Results:

  • The negative binomial mixed model demonstrated superior performance compared to Poisson and negative binomial models.
  • Identified four distinct clusters within the analyzed data.
  • Found significant positive correlations between patent numbers and: health technicians per 10,000 people, financial expenditure on science and technology, and patent applications per 10,000 health personnel.

Conclusions:

  • Healthcare innovation in Chinese tertiary public hospitals is positively associated with the availability of health technicians and investment in R&D.
  • The negative binomial mixed model is a robust tool for analyzing healthcare patent data.
  • Policy interventions focusing on workforce development and research funding may enhance healthcare innovation capacity.