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相关概念视频

Formulating and Validating Nursing Diagnosis II01:25

Formulating and Validating Nursing Diagnosis II

2.8K
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...
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Classification of Illness01:17

Classification of Illness

7.5K
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...
7.5K
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.
There are thirteen domains...
2.7K
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.
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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Hospitals-II00:59

Hospitals-II

782
Hospitals provide inpatient and outpatient services. Inpatient services provide care to patients that stay in the hospital for an extended period, ranging from days to months. Examples of inpatient services include intensive care units, hospital wards, or surgeries. Outpatient services provide care to patients who come to a hospital for a diagnostic or treatment but do not stay overnight —for example, diagnostic tests, surgical procedures, or health education.
Nurses that work in...
782
Diagnostic and Statistical Manual of Mental Disorders (DSM)01:27

Diagnostic and Statistical Manual of Mental Disorders (DSM)

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The Diagnostic and Statistical Manual of Mental Disorders (DSM) serves as the primary classification system for mental health disorders, providing standardized diagnostic criteria for clinicians and researchers. First published by the American Psychiatric Association (APA) in 1952, the DSM has undergone several revisions to reflect evolving psychiatric understanding. The fifth edition, DSM-5, released in 2013, introduced key updates that expanded diagnostic categories and modified diagnostic...
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相关实验视频

Updated: Jul 5, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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使用大型开放的临床体来改进ICD-10诊断编码.

Anastasios Lamproudis1, Therese Olsen Svenning1, Torbjørn Torsvik1

  • 1Norwegian Centre for E-health Research, Tromsø, Norway.

AMIA ... Annual Symposium proceedings. AMIA Symposium
|January 15, 2024
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概括

本研究介绍了用于训练人工智能模型的数据集,以帮助医学编码人员编码国际疾病分类第10版 (ICD-10) 诊断. 使用这些数据集的基于BERT的模型有效地将ICD-10代码分配给瑞典的排放摘要,从而减少了编码员的工作量.

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科学领域:

  • 自然语言处理自然语言处理.
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 医疗信息学 医疗信息学

背景情况:

  • 在NLP和深度学习方面的进步使得人工智能辅助的医学编码变得更加可行.
  • 有效的ICD-10诊断编码出院摘要对于医疗保健数据管理至关重要.

研究的目的:

  • 提出用于训练医疗编码AI模型的新型数据集.
  • 开发和评估一个基于BERT的语言模型,用于将ICD-10代码分配给瑞典解雇摘要.
  • 在一个实用的编码支持工具中展示这些数据集和模型的实用性.

主要方法:

  • 开发和策划用于医疗编码任务的专用数据集.
  • 培训一个基于BERT的语言模型,用瑞典语汇总出台的 absoluttsammendragningar.
  • 评估模型在分配ICD-10代码方面的表现.
  • 模拟编码支持工具,推代码以减少编码员的工作量.

主要成果:

  • 一个基于BERT的语言模型成功地使用所介绍的数据集进行了训练.
  • 该模型在将准确的ICD-10代码分配给瑞典出院摘要方面表现一致.
  • 开发的模型可以集成到编码支持系统中,以提高编码器的效率.

结论:

  • 提出的数据集是开发医疗编码人工智能的宝贵资源.
  • 像基于BERT的AI模型一样,可以通过减少医疗编程人员的工作量来显著帮助医疗编程人员.
  • 无标识和伪名的数据集可用于学术研究,以促进进一步的发展.