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Related Concept Videos

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...
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...
Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis01:24

Nursing Process for Patient and Caregiver Teaching I: Assessment and Diagnosis

The nursing process provides a clinical decision-making framework for patients and families to establish and implement a personalized care plan. Since part of the nurse's duties is to teach patients, the steps of the nursing process are the most effective way to approach instruction. The nursing process and the teaching-learning process are inextricably linked.
It is critical to determine the patient's learning needs during the assessment. Determination of learning needs compounds data from the...
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...
Role of Communication in the Nursing Process I: Assessment and Diagnosis01:25

Role of Communication in the Nursing Process I: Assessment and Diagnosis

The nursing process uses scientific reasoning, problem-solving, and critical thinking to guide nurses in providing patients with appropriate care. This process is a systematic approach to recognize, avoid, and treat current or potential health issues while promoting the patient's well-being.
The nursing process considers the patient's emotional and physical well-being. The process can be repeated or stopped at any point if judged essential. Assessment is the first step in the nursing process.
Data Validation01:03

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...

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A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis
04:19

A Computer-Based Platform for Aiding Clinicians in Eating Disorder Analysis and Diagnosis

Published on: May 10, 2022

Proposal of diagnostic process model for computer based diagnosis.

Yasushi Matsumura1, Toshihiro Takeda, Shiro Manabe

  • 1Medical Informatics, Osaka University Graduate School of Medicine, Osaka, Japan. matumura@hp-info.med.osaka-u.ac.jp

Studies in Health Technology and Informatics
|August 10, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a practical diagnostic support system using a simple knowledge model. It aids diagnosis by matching patient clinical findings (CFs) to a database of disease patterns.

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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Published on: January 11, 2020

Area of Science:

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

Background:

  • Developing practical diagnostic support systems is crucial for improving healthcare.
  • Existing systems may rely on complex knowledge bases, limiting their accessibility.
  • A simplified knowledge model can enhance the usability of diagnostic tools.

Purpose of the Study:

  • To propose a diagnostic process model based on simple, textbook-derived knowledge.
  • To create a practical diagnostic support system.
  • To define clinical findings (CFs) and CF patterns for diagnostic matching.

Main Methods:

  • Defined clinical finding (CF) as a Boolean value representing patient symptoms or findings.
  • Introduced "CF pattern" as a combination of CFs and a "case base" of CF patterns with concomitant diseases.
  • Modeled diagnosis as searching the case base for matching CF patterns, narrowing candidates by checking specific CF presence/absence.
  • Calculated CF pattern probability assuming CF independence, using disease frequency by age/sex and CF-disease occurrence rates.

Main Results:

  • The proposed model enables diagnosis by processing disease frequency and CF-disease relationships.
  • The system can identify candidate diseases based on patient CF patterns.
  • The method allows for narrowing down diagnostic possibilities effectively.

Conclusions:

  • A practical diagnostic support system can be developed using a simple knowledge model.
  • The model effectively utilizes readily available medical knowledge for diagnosis.
  • This approach offers a feasible method for computer-aided diagnosis.