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

Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Dosage Regimen Designs: Nomograms and Tabulations01:23

Dosage Regimen Designs: Nomograms and Tabulations

Nomograms and tabulations are vital tools used by clinicians to design accurate and individualized dosage regimens. These instruments provide a straightforward method for adjusting dosages based on individual patient characteristics, including age, weight, and physiological condition. The foundation of a drug's nomogram is population pharmacokinetic data collected and analyzed using specific models. This data simplifies complex equations, presenting them diagrammatically or tabularly for easy...
Dosage Regimens: Designs and Approaches01:28

Dosage Regimens: Designs and Approaches

Designing a dosage regimen, which refers to the manner of drug administration, is a complex process involving the selection of drug dose, route, and frequency. This process is underpinned by pharmacokinetic parameters derived from tests and population averages. These parameters are then tailored to patient-specific variables such as diagnosis, demographics, and allergy status. Once therapy commences, therapeutic response monitoring is critical and achieved through clinical and physical...
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

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...
Types of Reports III: Telephone and Verbal Reports01:26

Types of Reports III: Telephone and Verbal Reports

Telephone and Verbal Reports in healthcare settings are two communication methods for conveying therapeutic instructions from healthcare providers to nurses or other healthcare staff.
Here's an overview of each type:
Telephone Orders
Determination of Multiple Dosing Parameters: Loading and Maintenance Doses01:25

Determination of Multiple Dosing Parameters: Loading and Maintenance Doses

A loading dose is an essential pharmacological strategy to rapidly achieve the target plasma drug concentration necessary for an immediate therapeutic effect. This approach is especially critical for drugs characterized by slow absorption or extended half-lives, where delaying therapeutic plasma levels could compromise treatment outcomes. By administering a loading dose, clinicians ensure a prompt onset of drug action, even for agents with complex pharmacokinetic profiles.Achieving steady-state...

You might also read

Related Articles

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

Sort by
Same author

A Novel Approach to Pediatric Sleep Screening and Guidance in Primary Care.

JAMA network open·2025
Same author

Rating Recommendations in AAP Clinical Practice Guidelines: More Important Than You Might Think.

Pediatrics·2025
Same author

Real-world effectiveness and causal mediation study of BNT162b2 on long COVID risks in children and adolescents.

EClinicalMedicine·2024
Same author

Predicting food insecurity in a pediatric population using the electronic health record.

Journal of clinical and translational science·2024
Same author

Cost-Effectiveness of Intensive Blood Pressure Control in Youth With Chronic Kidney Disease.

Hypertension (Dallas, Tex. : 1979)·2024
Same author

The Study of the Epidemiology of Pediatric Hypertension Registry (SUPERHERO): rationale and methods.

American journal of epidemiology·2024

Related Experiment Video

Updated: Jun 4, 2026

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
08:25

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Published on: April 11, 2018

A method to compute treatment suggestions from local order entry data.

Jeffrey Klann1, Gunther Schadow, Stephen M Downs

  • 1Regenstrief Institute, Indianapolis, IN;

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|February 25, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an automated method for generating clinical decision support content using local data and Bayesian networks. The approach successfully identified key treatments for various conditions, improving clinical decision-making.

More Related Videos

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Related Experiment Videos

Last Updated: Jun 4, 2026

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System
08:25

Radiation Planning Assistant - A Streamlined, Fully Automated Radiotherapy Treatment Planning System

Published on: April 11, 2018

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

  • Medical Informatics
  • Clinical Decision Support
  • Artificial Intelligence in Healthcare

Background:

  • Manual maintenance of clinical decision support systems (CDSS) content is challenging, leading to low utilization.
  • Automated content generation for CDSS is needed to improve cost-effectiveness and patient care.
  • Existing CDSS often lack dynamic adaptation to local clinical practices.

Purpose of the Study:

  • To pilot an automated approach for generating clinical decision support content using local order entry data.
  • To leverage Bayesian networks for identifying multivariate associations and suggesting treatments.
  • To evaluate the accuracy and clinical relevance of automatically generated treatment suggestions.

Main Methods:

  • Utilized local order entry data from 5044 hospitalizations of pregnant women.
  • Selected 70 frequent order and treatment variables across 20 treatable conditions.
  • Employed Bayesian networks to automatically discover associations and generate treatment suggestion lists.

Main Results:

  • Treatment suggestion lists were generated for 15 of the 20 conditions.
  • Lists accurately captured clinical knowledge, including the key treatment (71% overall, 90% non-labor-related).
  • High predictive accuracy for treatments (average AUC .873) and pregnancy-specific treatments (AUC > .9) was achieved on test data.

Conclusions:

  • Automated generation of clinical decision support content is feasible and effective.
  • Bayesian networks can successfully identify clinically relevant treatment associations from local data.
  • This method offers a scalable approach to harness collective clinical intelligence for improved decision support.