Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Determination of Expected Frequency

Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.Positive Frequency-Dependent SelectionIn positive...

Expected Frequencies in Goodness-of-Fit Tests

Expected Frequencies in Goodness-of-Fit Tests

A goodness-of-fit test is conducted to determine whether the observed frequency values are statistically similar to the frequencies expected for the dataset. Suppose the expected frequencies for a dataset are equal such as when predicting the frequency of any number appearing when casting a die. In that case, the expected frequency is the ratio of the total number of observations (n) to the number of categories (k).

You might also read

Related Articles

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

Sort by

Same author

An Interoperability Moonshot Proposal.

Applied clinical informatics·2026

Same author

Clinical Policy: Critical Issues Related to Harms of Cannabis Exposure in Adult Patients Presenting to the Emergency Department, Cardiovascular Considerations: Approved by ACEP Board of Directors September 4, 2025.

Annals of emergency medicine·2025

Same author

Influences on Emergency Clinician Use of Health Information Exchange: Interview Study.

JMIR medical informatics·2025

Same author

Clinical Policy: Critical Issues in the Management of Adult Patients Requiring Endotracheal Intubation in the Emergency Department.

Annals of emergency medicine·2025

Same author

Clinical Policy: A Critical Issue in the Outpatient Management of Adult Patients Presenting to the Emergency Department With Asymptomatic Elevated Blood Pressure: Approved by the ACEP Board of Directors January 22, 2025.

Annals of emergency medicine·2025

Same author

Critical Issues in the Evaluation and Management of Adult Patients With Suspected Acute Nontraumatic Thoracic Aortic Dissection.

Annals of emergency medicine·2025

Search research articles

Related Experiment Video

Updated: Jul 2, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

A rationale for parsimonious laboratory term mapping by frequency.

Daniel J Vreeman¹, John T Finnell, J Marc Overhage

¹Regenstrief Institute, Inc., Indianapolis, IN, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium

|August 13, 2008

Summary

Mapping many local laboratory observation codes is resource intensive. Prioritizing high-volume codes significantly reduces mapping effort and improves healthcare data interoperability.

More Related Videos

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

Published on: April 1, 2016

Related Experiment Videos

Last Updated: Jul 2, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

Comparing the Frequency Effect Between the Lexical Decision and Naming Tasks in Chinese

Published on: April 1, 2016

Area of Science:

Health Informatics
Clinical Data Management
Biomedical Data Standards

Background:

Healthcare data resides in isolated systems, hindering interoperability.
Mapping local observation codes to standard vocabularies is crucial but resource-intensive.
Prioritization strategies are needed to optimize mapping efforts.

Purpose of the Study:

To analyze laboratory result data to identify high-yield observation codes.
To inform strategies for prioritizing code mapping to enhance data interoperability.
To assess the volume and patient coverage of frequently reported laboratory codes.

Main Methods:

Analysis of laboratory results from five institutions over thirteen months.
Quantification of the volume and frequency of over 4,000 laboratory observation codes.
Identification of codes representing 80%, 99%, and 99%+ of data volume and patient capture.

Main Results:

A small subset of codes (2% or 80 codes) accounted for 80% of total results.
A larger subset (19% or 784 codes) accounted for 99% of total results.
244-517 codes captured 99% of patient results at each institution.

Conclusions:

Focusing mapping efforts on high-yield codes is an efficient strategy.
This approach can significantly reduce the resources required for data standardization.
Prioritizing high-volume codes facilitates greater healthcare data interoperability.