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Related Concept Videos

Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

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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...
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Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

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The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
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Contingency Table01:29

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A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
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Classification of Illness01:17

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Formulating and Validating Nursing Diagnosis I01:26

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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...
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Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

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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.
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Updated: Sep 3, 2025

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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Co-occurrence patterns in diagnostic data.

Marie Ely Piceno1, Laura Rodríguez-Navas2, José Luis Balcázar1

  • 1Computer Science Department Universitat Politècnica de Catalunya (UPC) Barcelona Spain.

Computational Intelligence
|July 25, 2022
PubMed
Summary
This summary is machine-generated.

We visualize hierarchical medical data patterns using graph decomposition. This method reveals co-occurrence insights from datasets, aiding further statistical analysis.

Keywords:
Gaifman graphsclan decompositionexploratory data analysis

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Area of Science:

  • Graph theory
  • Data visualization
  • Medical informatics

Background:

  • Hierarchical co-occurrence patterns in medical data are complex.
  • Traditional statistical analysis may not fully capture these relationships.

Purpose of the Study:

  • To demonstrate graph decomposition for visualizing hierarchical co-occurrence patterns in medical data.
  • To leverage mathematical graph concepts for data analysis.

Main Methods:

  • Construction of Gaifman graphs from datasets based on item co-occurrence.
  • Application of graph decomposition techniques, including graph modules and clan decomposition.
  • Utilizing discretization on edge labels for different Gaifman graph variants.

Main Results:

  • Successful visualization of hierarchical co-occurrence patterns.
  • Identification of data co-occurrences through graph decomposition.
  • Generation of visual information preceding statistical analysis.

Conclusions:

  • Graph decomposition offers a novel approach for understanding complex medical data relationships.
  • This visualization technique enhances traditional statistical methods.
  • The method provides valuable insights into data co-occurrence structures.