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相关概念视频

Formulating and Validating Nursing Diagnosis I01:26

Formulating and Validating Nursing Diagnosis I

2.6K
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...
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Documentation of Nursing Diagnosis01:10

Documentation of Nursing Diagnosis

1.2K
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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Methods of Documentation V: CBE01:23

Methods of Documentation V: CBE

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Charting by Exception, or CBE, is a method of documentation used in healthcare, particularly in nursing, that focuses on documenting only significant or abnormal findings rather than recording every detail. This approach aims to streamline the documentation process, improve efficiency, and ensure that healthcare providers can quickly identify deviations from normalcy in patient assessments.
In CBE, healthcare professionals establish predefined standards of practice that define what constitutes...
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Methods of Documentation VII: EMR01:30

Methods of Documentation VII: EMR

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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...
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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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Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

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Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
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相关实验视频

Updated: Jun 25, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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使用临床文本来改进荷兰全科医生EHR数据中的不特定条件代码.

Tom M Seinen1, Jan A Kors1, Erik M van Mulligen1

  • 1Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, the Netherlands.

International journal of medical informatics
|May 31, 2024
PubMed
概括

通过使用临床文本在电子健康记录中改进非特定的临床代码可以提高数据的特异性. 这项研究表明,通过更好的数据细节,增强观察研究和患者护理的可行性.

关键词:
电子健康记录 (EHR) 是一种电子健康记录.机器学习 机器学习自然语言处理自然语言处理.初级医疗保健是一项主要的医疗保健.文本挖掘 (Text Mining) 是一个很好的方法.

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Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗数据分析 医学数据分析
  • 在医疗保健中的自然语言处理.

背景情况:

  • 电子健康记录 (EHR) 数据库对于观察性研究至关重要.
  • 非特定的临床代码限制了电子健康记录中的医疗信息的准确性.
  • 完善这些代码对于准确的数据分析和研究至关重要.

研究的目的:

  • 评估在荷兰全科医生 (GP) EHR数据库中增强非特定条件代码的可行性.
  • 为了提高代码特异性,利用临床自由文本.
  • 评估代码改进对数据分析的影响.

主要方法:

  • 使用的文本分类技术:搜索查询,半监督学习和监督学习.
  • 提高了十个不特定的国际初级保健分类 (ICPC-1) 代码的特异性.
  • 对 (半监督) 模型评估了两个文本表示和三个机器学习算法.

主要成果:

  • 在大多数代码中,分类模型通常表现良好.
  • 没有一个单一的方法始终优于其他方法;性能因代码和方法而异.
  • 阶级失衡和有限的数据影响了 (半) 监督模型,但搜索查询是有效的.
  • 超过一半的非特定代码出现显示了更好的特异性.

结论:

  • 使用临床文本信息是可行的,以增强EHR数据库中的非特定代码.
  • 即使有限的机器学习技术和适度的训练数据也能带来改进.
  • 未来的研究应该探索转移学习,结构化数据集成和交叉设定验证.
  • 增强数据细节有利于观察性研究和患者护理.