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

Prediction Intervals01:03

Prediction Intervals

3.6K
The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
3.6K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

340
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...
340
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

410
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...
410
Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

464
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.
464
Survival Tree01:19

Survival Tree

497
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...
497
Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

355
Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
355

You might also read

Related Articles

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

Sort by
Same author

Assessing comorbid PTSD, depression, and anxiety in fibromyalgia patients: a retrospective observational study.

BMC psychiatry·2025
Same author

Electroconvulsive therapy (ECT) and Psychiatric rehospitalization rates: a retrospective study.

BMC psychiatry·2024
Same author

Dihydroxyl-Cooperative 1,2,4-Triazole-Based Ionic Liquid for Robust Reversible CO<sub>2</sub> Absorption.

Langmuir : the ACS journal of surfaces and colloids·2024
Same author

Long-term cancer outcomes after bariatric surgery.

Obesity (Silver Spring, Md.)·2023
Same author

Arden Syntax on FHIR.

Studies in health technology and informatics·2023
Same author

Long-term all-cause and cause-specific mortality for four bariatric surgery procedures.

Obesity (Silver Spring, Md.)·2023
Same journal

LabSage: Structural-Semantic Decoupling for Enhanced Retrieval-Augmented Generation in Clinical Laboratories.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

Evaluating Representation Embeddings from LLMs and Time-Series Foundation Models for Wearable Accelerometer-Based Health Prediction.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

Mapping the Storm: Linking Tornado Paths to Emergency Room Surges Through Geocoded Patient Data.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

Multi-Modal Deep Learning-Based Model to Predict Burkitt Lymphoma Recurrence.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

A Multi-Model LLM Consensus Framework to Identify EHR-Predictable Eligibility Criteria in NSCLC Immunotherapy Trials.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
See all related articles

Related Experiment Video

Updated: Apr 12, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.1K

Ontology-based tools to expedite predictive model construction.

Peter Haug1, John Holmen2, Xinzi Wu2

  • 1Intermountain Healthcare, Salt Lake City, Utah ; University of Utah.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|May 9, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel system that combines medical ontologies with clinical data warehouses. This approach automates the creation of diagnostic models, accelerating medical research and data analysis.

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.8K

Related Experiment Videos

Last Updated: Apr 12, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

3.1K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

1.5K
An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

2.8K

Area of Science:

  • Medical Informatics
  • Health Data Science
  • Clinical Research Informatics

Background:

  • Modern medical information systems generate vast amounts of electronic patient data.
  • Leveraging this data for clinical research and developing predictive models is challenging due to significant expert effort required for data interpretation and analysis.
  • Existing methods for constructing computable diagnostic models are often time-consuming and resource-intensive.

Purpose of the Study:

  • To reduce the effort and time needed to build computable diagnostic predictive models.
  • To develop a system that automates preliminary diagnostic model construction.
  • To provide researchers with visual aids for planning further data analysis.

Main Methods:

  • Development of a hybrid system integrating a medical ontology with a large clinical data warehouse.
  • Implementation of components to automate the creation of preliminary diagnostic models.
  • Inclusion of features for generating visual clues to guide subsequent data analysis.

Main Results:

  • The developed system successfully automates key steps in diagnostic model construction.
  • Visual clues are generated to assist researchers in understanding and planning further analysis of clinical data.
  • The hybridization approach demonstrates potential for streamlining medical research workflows.

Conclusions:

  • The integrated system significantly reduces the manual effort required for developing diagnostic predictive models.
  • This approach accelerates the process of extracting insights from electronic health records.
  • The system offers a promising solution for enhancing the efficiency of medical data mining and observational studies.