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

Variability: Analysis01:11

Variability: Analysis

231
Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
231
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

143
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...
143
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

250
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...
250
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

9.3K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
9.3K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

103
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...
103
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

145
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...
145

You might also read

Related Articles

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

Sort by
Same author

Potential Adverse Drug Events Identified with Decision Support Algorithms from Janusmed Risk Profile-A Retrospective Population-Based Study in a Swedish Region.

Pharmacy (Basel, Switzerland)·2024
Same author

An Ontology to Bridge the Clinical Management of Patients and Public Health Responses for Strengthening Infectious Disease Surveillance: Design Science Study.

JMIR formative research·2024
Same author

Personalized Assistive Technologies for Motor-Impaired Students: A Case of Learning Process Mining.

Studies in health technology and informatics·2023
Same author

DIdM-EIoTD: Distributed Identity Management for Edge Internet of Things (IoT) Devices.

Sensors (Basel, Switzerland)·2023
Same author

Pharmacological Risk Assessment Among Older Patients with Polypharmacy Using the Clinical Decision Support System Janusmed Risk Profile: A Cross-Sectional Register Study.

Drugs & aging·2023
Same author

[Janusmed Renal Function, an appreciated CDSS to support prescription of drugs and appropriate dosing in patients with renal impairment].

Lakartidningen·2022

Related Experiment Video

Updated: Oct 9, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.1K

dfgcompare: a library to support process variant analysis through Markov models.

Amin Jalali1, Paul Johannesson2, Erik Perjons2

  • 1Department of Computer and Systems Sciences (DSV), Stockholm University, 16407, Stockholm, Sweden. aj@dsv.su.se.

BMC Medical Informatics and Decision Making
|December 21, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces dfgcompare, an open-source Python library for comparing process cohorts using Markov models. It enables detailed analysis of process variants, crucial for healthcare insights.

Keywords:
Markov chainProcess miningProcess variant analysis

More Related Videos

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

10.0K
Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

34.1K

Related Experiment Videos

Last Updated: Oct 9, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
07:15

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

11.1K
Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
09:37

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information

Published on: August 15, 2019

10.0K
Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease
09:34

Targeted Next-generation Sequencing and Bioinformatics Pipeline to Evaluate Genetic Determinants of Constitutional Disease

Published on: April 4, 2018

34.1K

Area of Science:

  • Computer Science
  • Data Science
  • Process Mining

Background:

  • Data-driven process analysis requires software support for comparing process variants (cohorts).
  • Existing tools focus on activity frequencies and performance metrics, lacking support for transition probabilities.
  • Transition probabilities are vital for advanced cohort analysis, especially in healthcare.

Purpose of the Study:

  • To define and implement an approach for comparing process cohorts using Markov models.
  • To provide an open-source software library (dfgcompare) for researchers.
  • To enable cohort comparison based on state transition probabilities.

Main Methods:

  • Formalization of a Markov model-based approach for process cohort comparison.
  • Implementation of the approach as an open-source Python library named dfgcompare.
  • Application of the library to analyze caregiver prescribing behavior.

Main Results:

  • The dfgcompare library successfully implements the Markov model approach.
  • The approach enables practical comparison of process cohorts.
  • Analysis of caregiver behavior in drug prescription demonstrated the library's utility.

Conclusions:

  • dfgcompare effectively supports the identification of differences among process cohorts.
  • The developed approach enhances process analysis capabilities, particularly in healthcare.
  • The open-source nature of dfgcompare promotes wider adoption and research.