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

Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

The nurse documents nursing diagnoses and enters them into the patient record. The identified patient's nursing diagnosis is either written out with a plan of care or entered into the electronic health record.
In some settings, data-driven computerized decision support systems are in place, allowing for more accurate nursing diagnoses. The database within one of these systems includes diagnostic labels defining characteristics, activities, and indicators for nursing. A nurse enters assessment...
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

Nursing diagnoses represent a problem validated by major defining characteristics. There are four categories of nursing diagnoses: problem-focused, risk, health promotion or wellness, and syndrome. The anatomy of a nursing diagnosis includes three components: problem statement or diagnostic label, defining characteristics, and related factors.
Risk nursing diagnoses represent clinical judgments of an individual, family, or community more vulnerable to developing the health problem than others...
Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

A nursing diagnosis is written when the nurse recognizes a cluster of essential patient data indicating health problems treated with independent nursing interventions. The standardized terminologies of a nursing diagnosis help nurses identify and treat patients' problems. Every electronic health record that uses nursing diagnosis must employ standard diagnostic terminology. Developing an efficient, individualized care plan begins with accurate nursing diagnoses.
There are thirteen domains for...
Nursing Diagnosis01:22

Nursing Diagnosis

Following assessment, a nursing diagnosis is the next step in the nursing process. It begins after the nurse has collected and recorded the patient data. The purpose of diagnosing is to identify how the client responds to actual or potential health processes, identify factors that bestow or that cause health problems, the etiologies, and identify resources or strengths the individual, group, or community can draw on to prevent or resolve problems.
The nursing diagnosis focuses on evidence-based...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Receiver Operating Characteristic Plot01:15

Receiver Operating Characteristic Plot

A ROC (Receiver Operating Characteristic) plot is a graphical tool used to assess the performance of a binary classification model by illustrating the trade-off between sensitivity (true positive rate) and specificity (false positive rate). By plotting sensitivity against 1 - specificity across various threshold settings, the ROC curve shows how well the model distinguishes between classes, with a curve closer to the top-left corner indicating a more accurate model. The area under the ROC curve...

You might also read

Related Articles

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

Sort by
Same author

Personalized Antibiograms Using Multi-Task Machine Learning: Toward Mechanistic Understanding and Robust Calibration.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2026
Same author

Personalized Antibiogram: A Novel Multi-Task Machine Learning Framework for Simultaneous Prediction of Antimicrobial Resistance Profile with Enhanced Detection of Carbapenem Resistance in Enterobacteriaceae.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America·2026
Same author

Public Health.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2025
Same author

Automate Creating, Customizing, and Optimizing Comorbidity Indices Using a Data-Driven AI/ML Approach.

Studies in health technology and informatics·2025
Same author

A Primer for Big Data Research: A Protocol Reflection.

The Journal of neuroscience nursing : journal of the American Association of Neuroscience Nurses·2025
Same author

Predicting Nephrotoxic Acute Kidney Injury in Hospitalized Adults: A Machine Learning Algorithm.

Kidney medicine·2024
Same journal

Sensitivity Analyses of a Scoring System for a Contraception Decision Aid.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Improving electronic health record processing of large language models via retrieval-augmented generation: A case study on dietary supplements.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Developing a User-Centered Mobile Application Prototype: Bridging Lower-Limb Fracture Care from Skilled Nursing Facility and Back to the Community.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Automating Adjudication of Cardiovascular Events Using Large Language Models.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
Same journal

Predictive Factors and State-Level Barriers to Postpartum Birth Control Usage in the United States: Insights from PRAMS Phase 8.

AMIA ... Annual Symposium proceedings. AMIA Symposium·2026
See all related articles

Related Experiment Video

Updated: Jun 28, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

The optimal diagnostic decision sequence.

Chih-Lin Chi1, W Nick Street

  • 1Health Informatics Program, University of Iowa, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|November 13, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a data mining model using heuristic search and genetic algorithms to create optimal diagnostic sequences for cost-effective decisions. Genetic algorithms effectively identify superior sequences by avoiding local optima, enhancing decision-making efficiency.

Related Experiment Videos

Last Updated: Jun 28, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Decision Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Sequential decision-making in diagnostics often involves complex choices.
  • Optimizing diagnostic pathways is crucial for cost-effectiveness and accuracy.
  • Existing methods may struggle with local optima in sequence construction.

Purpose of the Study:

  • To develop a data mining model for constructing optimal diagnostic sequences.
  • To enhance cost-effective sequential decision-making processes.
  • To leverage heuristic search and machine learning for improved diagnostic pathway selection.

Main Methods:

  • Utilized heuristic search algorithms, including hill climbing and genetic algorithms (GAs).
  • Employed a cost-based Mean Accuracy Gain (cMAG) evaluation function.
  • Integrated Support Vector Machine (SVM) classifiers to provide the cMAG evaluation.

Main Results:

  • The developed model successfully constructs optimal diagnostic sequences.
  • Genetic algorithms demonstrated effectiveness in finding superior sequences.
  • The approach facilitates cost-effective sequential decision-making.

Conclusions:

  • The data mining model provides an effective method for optimizing diagnostic sequences.
  • Genetic algorithms are well-suited for navigating complex search spaces in diagnostic pathway selection.
  • This approach offers a pathway to more efficient and cost-effective healthcare decisions.