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Related Concept Videos

Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

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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.
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Formulating and Validating Nursing Diagnosis II01:25

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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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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.
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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.
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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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Intellectual disability (ID) is a neurodevelopmental condition characterized by deficits in intellectual and adaptive functioning that manifest during the developmental period. This condition encompasses challenges in reasoning, memory, problem-solving, and learning, accompanied by impairments in everyday life skills, such as communication, self-care, and social interactions. Intellectual disability affects approximately 1% of the population in the United States, impacting an estimated 5...
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Diagnosis knowledge constrained network based on first-order logic for syndrome differentiation.

Meiwen Li1, Lin Wang1, Qingtao Wu1

  • 1School of Information Engineering, Henan University of Science and Technology, Luoyang, 471023, China.

Artificial Intelligence in Medicine
|December 3, 2023
PubMed
Summary

This study introduces a novel deep network model for Traditional Chinese Medicine (TCM) syndrome differentiation, achieving 89% accuracy. The model integrates TCM knowledge into deep learning, outperforming existing methods and offering a new dataset for research.

Keywords:
Deep learningFirst-order logicSyndrome differentiationTraditional Chinese medicine

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Area of Science:

  • Integrative Medicine
  • Artificial Intelligence in Healthcare
  • Computational Linguistics

Background:

  • Traditional Chinese Medicine (TCM) relies on syndrome differentiation, a process heavily dependent on physician experience.
  • Machine learning advancements offer potential solutions to enhance the objectivity and consistency of TCM syndrome differentiation.
  • Existing deep learning and traditional machine learning models have limitations in capturing the nuances of TCM diagnostic principles.

Purpose of the Study:

  • To develop and evaluate a novel deep network model for Traditional Chinese Medicine syndrome differentiation.
  • To improve the accuracy and reliability of TCM syndrome differentiation by integrating domain knowledge.
  • To introduce a comprehensive dataset for TCM syndrome differentiation research.

Main Methods:

  • Proposed a deep network model incorporating Traditional Chinese Medicine syndrome differentiation knowledge via first-order logic.
  • Developed and utilized the Traditional Chinese Medicine Syndrome Differentiation (TSD) dataset, containing over 40,000 clinical records.
  • Compared the proposed model's performance against Multi-Layer Perceptron (MLP) and other traditional machine learning models.

Main Results:

  • The proposed deep network model achieved an accuracy of 89% in TCM syndrome differentiation.
  • The model significantly outperformed traditional machine learning models and the MLP deep learning model.
  • The TSD dataset provides detailed labeling of diseases, syndromes, and patterns, facilitating further research.

Conclusions:

  • The novel deep network model effectively enhances TCM syndrome differentiation accuracy.
  • Integrating TCM knowledge into deep learning models is a promising approach for objective diagnosis.
  • The TSD dataset represents a significant resource for advancing research in TCM syndrome differentiation and exploring complex medical relationships.