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相关概念视频

Respiratory System Abnormal Finding II: Palpation and Auscultation01:31

Respiratory System Abnormal Finding II: Palpation and Auscultation

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In assessing respiratory abnormalities, palpation and auscultation are critical tools for detecting and interpreting various pathophysiological changes. These techniques provide insight into underlying disorders by evaluating tactile sensations and sounds produced by the respiratory system.
Palpation Findings
During a respiratory assessment, palpation can reveal several vital abnormalities:
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Physical Assessment of the Respiratory Tract IV: Auscultation01:28

Physical Assessment of the Respiratory Tract IV: Auscultation

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Auscultation is a crucial component of the physical assessment of the respiratory tract. It offers valuable insights into airflow through the bronchial tree and potential lung obstructions. This process involves careful listening to breath, voice, and adventitious sounds, which can reveal a wealth of information about a patient's respiratory health.
Breath Sounds
Breath sounds are categorized into vesicular, bronchovesicular, and bronchial.
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Common Respiratory Disorders01:31

Common Respiratory Disorders

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Respiratory disorders, a prevalent health concern globally, are generally divided into two primary categories: upper and lower respiratory tract disorders. The categorization is based on the area of the respiratory system they affect.
Upper respiratory disorders impact the airways above the vocal cords, encompassing areas like the nose, sinuses, and throat. Various conditions fall under this category, including the common cold and allergic rhinitis. These disorders can stem from several causes,...
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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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Assessment of Respiration01:23

Assessment of Respiration

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The respiratory system's basic structures and primary functions lay the foundation for nurses' comprehensive respiratory assessments. This assessment includes subjective and objective data to gauge the patient's respiratory health.
Subjective Assessment: Nurses interview the patient to gather information directly during the subjective assessment. It includes questions about the individual's medical history, medications, and symptoms, focusing on past respiratory conditions like...
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Respiratory symptoms, such as congestion and cough, commonly accompany respiratory tract conditions. Various medications, such as antitussives, expectorants, and mucolytics, play crucial roles in providing relief.
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相关实验视频

Updated: Jul 28, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

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环境知识驱动的通用模型从音频录音中检测咳.

Sudip Vhaduri1, Sayanton V Dibbo2, Yugyeong Kim3

  • 1Department of Computer and Information TechnologyPurdue University West Lafayette IN 47907 USA.

IEEE open journal of engineering in medicine and biology
|May 31, 2023
PubMed
概括

使用麦克风传感的机器学习模型可以通过分析咳来帮助诊断COVID-19等呼吸道疾病. 利用环境知识的引导模型比非引导方法准确度高出28%.

关键词:
音频分析 音频分析慢性慢性肺炎是一种慢性慢性肺炎,COPD是一种慢性肺炎.在 COVID-19 疫情中,咳 咳 咳 咳 咳 咳 咳 咳麦克风传感器可以传感到麦克风.

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Establishment of a Mouse Model with Cough Hypersensitivity via Inhalation of Citric Acid

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科学领域:

  • 生物医学工程 生物医学工程
  • 人工智能的人工智能
  • 呼吸系统医学 呼吸系统医学

背景情况:

  • 呼吸系统疾病,包括COVID-19和喘,影响全球数百万人.
  • 咳是一种常见的症状,需要客观地监测诊断和护理.

研究的目的:

  • 开发和评估适应环境的机器学习模型,以客观报告咳症状.
  • 评估麦克风传感器在帮助呼吸系统疾病诊断中的有用性.

主要方法:

  • 开发了三种机器学习方法:无引导,半引导和引导.
  • 模型被训练并使用多个数据集进行测试,包括开源的咳数据.
  • 这些方法考虑了用户对声环境知识的不同水平.

主要成果:

  • 与非引导模型相比,引导机器学习模型的准确性高达28%.
  • 这些模型在各种数据集中显示了适用的性能,验证了它们的概括性.
  • 准确度的提高与模型中纳入的环境知识程度相关.

结论:

  • 导向机器学习模型在呼吸道疾病检测中为咳分析提供了卓越的性能.
  • 导向模型的有效实施需要对周围的声环境有全面的了解.
  • 这种方法有可能通过客观的症状评估来改善患者护理.