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相关概念视频

Generalized Anxiety Disorder01:30

Generalized Anxiety Disorder

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Generalized Anxiety Disorder (GAD) is a chronic condition characterized by excessive and uncontrollable worry that persists for at least six months, significantly interfering with daily functioning. Unlike situational anxiety, which arises in response to specific stressors, GAD often occurs without a clear cause. Individuals may experience disproportionate worry about work, health, or relationships. For instance, a person might continuously fear poor health despite normal medical evaluations or...
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Classification of Illness01:17

Classification of Illness

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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.
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...
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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...
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Assessment of the Gastrointestinal System I: Subjective Data01:17

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Assessing the gastrointestinal (GI) system is a complex process that begins with collecting subjective data. This data, collected through patient interviews, provides crucial insights into the patient's health history, perception patterns, and lifestyle habits, all contributing significantly to GI health.
Health History
The initial step in assessing the GI system is obtaining a comprehensive health history. This includes inquiring about the patient's history or presence of problems...
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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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Assessment of the Gastrointestinal System II: Health Perception Pattern

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Assessing the gastrointestinal (GI) system is a complex process that begins with collecting subjective data. This data, collected through patient interviews, provides crucial insights into the patient's health history, perception patterns, and lifestyle habits, all contributing significantly to GI health.
Health Perception Patterns
Health perception patterns offer valuable insights into a patient's lifestyle habits and how they may impact their GI health. These patterns include:
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通过忘记进行概括 - 在临床笔记中用于提取症状事件的域概括.

Sitong Zhou1, Kevin Lybarger2, Meliha Yetisgen1

  • 1University of Washington, Seattle, WA, USA.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
|June 23, 2023
PubMed
概括

这项研究引入了一个域泛化方法,用于从临床笔记中提取症状,提高信息可访问性. 该方法通过使用掩面语言模型和适应性预训练,提高了在各种医疗保健环境中提取症状的准确性.

科学领域:

  • 自然语言处理自然语言处理.
  • 临床信息学 临床信息学
  • 生物医学数据科学 生物医学数据科学

背景情况:

  • 临床症状信息通常在自由文本注释中没有结构,限制了其在下游应用中的使用.
  • 现有的信息提取方法与不同机构和专业的临床语言的变化作斗争.

研究的目的:

  • 开发一个域泛化方法,从临床笔记中准确地提取症状.
  • 提高症状提取模型适应新和未见的临床领域的适应性.

主要方法:

  • 使用基于变压器的联合实体和关系提取模型.
  • 实施了一个域泛化技术,涉及源域中频繁的症状单词的动态掩盖.
  • 在与任务相关的未标记文本上使用变压器语言模型的适应性预训练.

主要成果:

  • 提出的掩盖和自适应预训练方法显著改善了症状提取性能.
  • 当源域和目标域差异很大时,性能增长最为显著.
  • 该方法在处理临床语言变异方面表现出更强大的稳定性.

结论:

  • 域泛化技术是有效的,以改善从各种临床笔记中提取症状.

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  • 适应性预训练和动态掩盖是构建更可泛化的临床NLP模型的关键策略.
  • 这项工作有助于更好地获取和利用医疗保健中的症状数据.