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Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.4K
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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Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

3.3K
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...
3.3K
Nursing Diagnosis01:22

Nursing Diagnosis

3.3K
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...
3.3K
Classification of Illness01:17

Classification of Illness

8.1K
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.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.1K
Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

3.2K
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...
3.2K
Levels of Health Promotion and Illness Prevention01:26

Levels of Health Promotion and Illness Prevention

13.7K
Health promotion allows a person to control the determinants of health, resulting in an improved health status. It enhances the quality of life and reduces premature deaths. Health promotion and illness prevention programs help people make beneficial choices to reduce the risk of disease and disabilities. There are three health promotion and illness prevention levels: primary, secondary, and tertiary prevention.
In primary prevention, actions taken before disease onset prevent the disease from...
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Intelligent Disease Prediagnosis Only Based on Symptoms.

Fangfang Luo1, Xu Luo2

  • 1School of Nursing, Zunyi Medical University, Zunyi 563000, China.

Journal of Healthcare Engineering
|August 12, 2021
PubMed
Summary

This study demonstrates that artificial intelligence can predict diseases using only symptoms. This enables intelligent medical triage and diagnosis of common illnesses.

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • Understanding the relationship between symptoms and diseases is crucial for medical advice.
  • Existing diagnostic methods can be time-consuming and require expert interpretation.
  • The need for efficient and accurate preliminary disease identification is growing.

Purpose of the Study:

  • To develop and validate computational methods for disease recognition based solely on patient symptoms.
  • To explore the efficacy of different machine learning algorithms in classifying diseases at various levels of specificity.
  • To assess the potential of symptom-based prediction for intelligent medical triage and common disease diagnosis.

Main Methods:

  • Medical data were categorized into main disease groups, subclass types, and specific diseases.
  • Two distinct symptom-based disease recognition methods were implemented: a neural network and a support vector machine (SVM).
  • The accuracy of both the neural network and SVM algorithms was rigorously tested and compared for disease identification.

Main Results:

  • The study successfully demonstrated the feasibility of automatic disease prediction using only symptom data.
  • Both the neural network and support vector machine (SVM) algorithms showed promising results in identifying diseases.
  • Comparative analysis confirmed the accuracy of symptom-based prediction for different disease classification levels.

Conclusions:

  • Automatic disease prediction from symptoms is achievable, supporting intelligent medical triage.
  • Symptom-based diagnostic tools can aid in the efficient identification of common diseases.
  • Machine learning approaches offer a viable pathway for enhancing preliminary medical assessments.