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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.
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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.
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Positive Symptoms of Schizophrenia: Hallucinations and Delusions01:30

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Schizophrenia is a complex mental health disorder that can manifest with various positive symptoms, including thought, movement, and behavior disorders. These symptoms significantly disrupt cognitive and motor functions, leading to profound effects on an individual's ability to engage with the world.
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Dementia01:30

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Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
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Positive Symptoms Schizophrenia: Hallucinations and Delusions01:26

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Schizophrenia is a complex psychiatric disorder characterized by a range of symptoms that significantly impact cognition, behavior, and emotional regulation. Among these, the positive symptoms stand out as they involve the addition or exaggeration of normal mental functions, deviating markedly from typical behavior and perception. Hallucinations and delusions are prominent positive symptoms, each profoundly affecting the individual's experience of reality.
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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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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Identifying Symptoms of Delirium from Clinical Narratives Using Natural Language Processing.

Aokun Chen1, Daniel Paredes1, Zehao Yu1

  • 1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.

Proceedings. IEEE International Conference on Healthcare Informatics
|December 27, 2024
PubMed
Summary

This study developed a novel natural language processing (NLP) system to extract delirium symptoms from clinical notes, improving recognition and diagnosis of this acute cognitive condition.

Keywords:
clinical natural language processingdeep learningdeliriummedication information extractionnamed entity recognition

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Area of Science:

  • Medical Informatics
  • Clinical Natural Language Processing
  • Computational Linguistics

Background:

  • Delirium, an acute cognitive impairment, often goes unrecognized in electronic health records (EHRs) due to its transient and varied presentation.
  • Accurate identification of delirium symptoms is crucial for timely diagnosis and management, yet current methods are limited.
  • Natural Language Processing (NLP) offers a promising avenue for extracting complex medical information from unstructured clinical text.

Purpose of the Study:

  • To develop and evaluate advanced NLP methods for accurate extraction of diverse delirium symptoms from clinical narratives.
  • To establish a robust system for aiding in the diagnosis and phenotyping of delirium.
  • To lay the groundwork for computable phenotypes and automated diagnosis of delirium.

Main Methods:

  • An expert panel curated delirium symptoms, developed annotation guidelines, and created a specialized delirium corpus.
  • Five state-of-the-art transformer models, including general (BERT, RoBERTa) and clinical (BERT_MIMIC, RoBERTa_MIMIC, GatorTron) domains, were compared.
  • NLP models were trained and evaluated on the curated delirium corpus for symptom extraction.

Main Results:

  • The GatorTron model demonstrated superior performance, achieving the highest strict F1 score of 0.8055 and lenient F1 score of 0.8759.
  • Error analysis identified key challenges in delirium symptom annotation and NLP system development.
  • This represents the first large language model-based system for delirium symptom extraction.

Conclusions:

  • The developed NLP system significantly enhances the capability to extract delirium symptoms from clinical notes.
  • This work provides a foundation for future advancements in computable delirium phenotypes and diagnostic tools.
  • Improved NLP-driven delirium detection can lead to better patient outcomes and more efficient healthcare.