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相关概念视频

Nursing Diagnosis01:22

Nursing Diagnosis

4.3K
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...
4.3K
Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

3.9K
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...
3.9K
Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

3.8K
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...
3.8K
Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.8K
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...
1.8K
Guidelines for Nursing Documentation I01:30

Guidelines for Nursing Documentation I

2.5K
Quality documentation and reporting share essential characteristics that ensure they are practical and valuable resources for those who use them. These characteristics are:
Factual:  
The following points emphasize the significance of upholding accurate and unbiased documentation in healthcare.
2.5K
Dementia l: Introduction01:22

Dementia l: Introduction

35
Dementia is an acquired, progressive syndrome characterized by a decline in multiple cognitive domains severe enough to impair daily functioning and reduce independence. Although memory loss is a central feature, the diagnosis requires additional deficits involving language, executive function, visuospatial skills, judgment, calculation, or abstract reasoning. These cognitive impairments reflect underlying neurodegenerative or vascular processes that gradually disrupt neuronal networks...
35

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A Human-Centered, Multi-Stage Clinical Decision Pathway for an AI-Based CDSS for Delirium Prevention.

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相关实验视频

Updated: May 3, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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从使用大型语言模型的护理报告中识别 Delirium.

Lisa Graf1,2, Alexander Ritzi3,4, Lili M Schoeler3,5

  • 1Neurorobotics Lab, Department of Computer Science - University of Freiburg, Germany.

Studies in health technology and informatics
|May 17, 2025
PubMed
概括

这项研究探讨了使用大型语言模型来检测护理报告中的妄想症. 微调 Phi3 模型实现了最高的准确性,显著优于其他方法来完成这一关键的临床任务.

关键词:
疯狂的 Delirium 是一个很好的方法.电子健康记录电子健康记录大型语言模型

更多相关视频

Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
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Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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相关实验视频

Last Updated: May 3, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

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Lexical Decision Task for Studying Written Word Recognition in Adults with and without Dementia or Mild Cognitive Impairment
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科学领域:

  • 人工智能的人工智能
  • 临床信息学 临床信息学
  • 自然语言处理自然语言处理.

背景情况:

  • 从非结构化的护理报告中检测 Delirium 是一个挑战.
  • 需要自动化方法来提高准确性和效率.
  • 大型语言模型 (LLM) 显示出临床文本分析的前景.

研究的目的:

  • 在护理笔记中评估LLM用于错觉检测.
  • 为了比较关键字匹配,提示和微调方法.
  • 为此任务确定最有效的LLM策略.

主要方法:

  • 使用来自德国弗赖堡大学医院的手动标记数据集.
  • 测试了Llama3和Phi3的大型语言模型.
  • 在关键字匹配,提示和微调技术中比较性能.

主要成果:

  • 提示和微调LLM都对错觉检测有效.
  • 微调 Phi3 (3.8B) 模型的精度是最高的 (90.24%).
  • 菲3微调也获得了最好的AUROC (96.07%),优于其他方法.

结论:

  • LLM,特别是像 Phi3 这样的微调模型,对于自动化妄检测非常有效.
  • 微调提供了比提示和关键字匹配更高的性能.
  • 这种方法可以通过更好地识别妄想来加强临床决策和患者护理.