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

Schemas01:42

Schemas

A schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

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...
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...
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.

You might also read

Related Articles

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

Sort by
Same author

A gold-standard French-language annotated corpus of oncological entities with ICD-O normalisation.

Scientific data·2026
Same author

Clarifying the relationship between biomedical and health informatics and digital health: expert perspectives.

BMJ health & care informatics·2026
Same author

QT prolongation alerts lead to monitoring but rarely to therapeutic changes: a prospective hospital study.

Frontiers in pharmacology·2026
Same author

Can LLMs Turn French PET/CT Narrative Reports into Structured Knowledge?

Studies in health technology and informatics·2026
Same author

Explainable Framework for Ontology-Based Similarity: A Use Case on SNOMED CT.

Studies in health technology and informatics·2026
Same author

Advancing Knowledge in Evaluating the Clinical Impact of Large Language Models for Clinical Text Summarization: A Narrative Review.

Studies in health technology and informatics·2026

Related Experiment Video

Updated: May 24, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Structured Chaos: Unintended Consequences of Over-Modeling.

Christophe Gaudet-Blavignac1,2, Julien Ehrsam1,2, Adel Bensahla Talet1,2

  • 1Division of Medical Information Sciences, Geneva University Hospitals, Geneva, Switzerland.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary

Structuring electronic health data faces an interoperability paradox. Robust data governance, versioning, and semantic standardization, not new models, are key to resolving semantic failures in clinical data elements.

Keywords:
Interoperabilitydata modelssemantics

Related Experiment Videos

Last Updated: May 24, 2026

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

Area of Science:

  • Health Informatics
  • Data Science
  • Clinical Data Management

Background:

  • Multiple models for structuring electronic health data have led to an interoperability paradox.
  • Semantic failures hinder the effective use of aggregated clinical data elements.

Purpose of the Study:

  • To identify the root causes of semantic failure in structured electronic health data.
  • To propose solutions for improving data interoperability and semantic consistency.

Main Methods:

  • Analysis of over 140,000 aggregated clinical data elements.
  • Examination of challenges including semantic drift, lack of traceability, and loss of context.

Main Results:

  • Semantic drift from non-versioned dictionaries is a key challenge.
  • Missing traceability from attribute modification contributes to data inconsistency.
  • Loss of context due to absent form layout in data warehouses exacerbates semantic failure.

Conclusions:

  • The creation of new models is not the solution to the interoperability paradox.
  • Implementing robust data governance is essential for semantic standardization.
  • Mandated versioning and semantic-centered approaches are crucial for resolving semantic failures in electronic health data.