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Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

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The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
The agent-host-environment model states that disease results...
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Models of Health Promotion and Illness Prevention I01:25

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A model is a theoretical way to understand a concept or an idea. Models can overcome barriers to health regardless of diverse economic and cultural backgrounds. In addition, models make the task easier by providing different ways to approach complex issues. There are two major health promotion models: the health belief model and the health promotion model.
The health belief model (HBM) attempts to predict health-related behavior in specific belief patterns. According to the HBM, a person's...
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Steps in Outbreak Investigation01:18

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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COPD: Pathogenesis and Clinical Features01:20

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Chronic obstructive pulmonary disease (COPD) is a group of lung conditions that progressively worsen over time, including chronic bronchitis and emphysema. This cluster of diseases collectively leads to a gradual and irreversible decline in lung function over time.
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Factors Affecting Illness01:18

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When a person's physical, emotional, intellectual, social development or spiritual functioning is compromised, this deviation from a healthy normal state is called illness. Illness creates stress that in turn harms individuals. Irritation, anger, denial, hopelessness, and fear are behavioral and emotional changes an individual experiences in the phases of illness. A variety of factors influence a person's health and well-being.
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Assessing and diagnosing Chronic Obstructive Pulmonary Disease (COPD) involves a detailed approach that includes a comprehensive review of medical history, physical examination, and a variety of diagnostic tests. This thorough evaluation is essential to ensure an accurate diagnosis and guide effective management strategies.
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长期COVID的预测模型

Blessy Antony1, Hannah Blau2, Elena Casiraghi3

  • 1Department of Computer Science, Virginia Polytechnic Institute and State University (Virginia Tech), Blacksburg, VA, 24061, USA.

EBioMedicine
|September 6, 2023
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概括

机器学习模型有效地使用电子健康记录预测长期COVID. 关键预测因素包括人口统计,症状和急性感染期间的药物,有助于早期识别.

关键词:
在 COVID-19 疫情中,分类 分类 分类 分类.跨站点的分析.可以解释的可解释性.长时间的COVID.

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科学领域:

  • 医疗信息学 医疗信息学
  • 计算生物学 计算生物学
  • 流行病学 流行病学

背景情况:

  • 长期COVID的原因和症状仍然不清楚,这使得未来的预测变得困难.
  • 早期识别风险COVID-19患者对于及时干预至关重要.

研究的目的:

  • 开发和评估用于预测长期COVID发病率的机器学习模型.
  • 确定与长期COVID发展相关的关键临床和人口因素.

主要方法:

  • 利用了国家COVID队列协作电子健康记录 (EHR) 数据.
  • 训练后勤回归 (LR) 和随机森林 (RF) 机器学习模型.
  • 包括诸如急性感染症状,药物,并发症和人口统计等特征;长期COVID由U09.9 ICD10-CM代码定义.

主要成果:

  • 雷射和射频模型的AUROC中位数分别为0.76和0.75.
  • 药物显著影响了预测的准确性;SHAP分析强调了年龄,性别,咳,疲劳,阿尔布特罗尔,肥胖,糖尿病和慢性肺部疾病.
  • 跨站点验证证明了模型的通用性,平均AUROC为0.75.

结论:

  • 使用急性感染EHR数据进行机器学习分类,对预测长期COVID有效.
  • SHAP分析确定了关键的预测特征,提供了对长期COVID风险因素的见解.
  • 开发的方法表明,它有望在不同的医疗保健系统中广泛应用.