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相关概念视频

Primary Healthcare Services01:30

Primary Healthcare Services

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Primary care promotes wellness and prevents disease. This care includes health promotion, education, protection (such as immunizations), early disease screening, and environmental considerations. Settings providing this type of healthcare include physician offices, public health clinics, school nursing, and community health nursing.
In 1978, international leaders convened in Alma-Ata, Kazakhstan, for what would be a pivotal event in global health. The Alma-Ata Declaration was the first to call...
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Levels of Health Promotion and Illness Prevention01:26

Levels of Health Promotion and Illness Prevention

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Health promotion allows a person to control the determinants of health, resulting in an improved health status. It enhances the quality of life and reduces premature deaths. Health promotion and illness prevention programs help people make beneficial choices to reduce the risk of disease and disabilities. There are three health promotion and illness prevention levels: primary, secondary, and tertiary prevention.
In primary prevention, actions taken before disease onset prevent the disease from...
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Preventive Healthcare Services01:30

Preventive Healthcare Services

1.8K
Preventive healthcare services keep people healthy via frequent check-ups, screening, and counseling. They primarily aid in disease prevention rather than treating an acute or chronic illness. Preventive treatment also keeps individuals productive and energetic, allowing them to work well into their retirement years. Examples of preventive care services include:
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Principles of Disease Surveillance01:26

Principles of Disease Surveillance

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Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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Healthcare Agencies II01:17

Healthcare Agencies II

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There are various healthcare agencies in the United States—some of which are managed by religious institutions and others by different government branches.
Parish nursing is a growing specialty nursing profession that focuses on holistic healthcare, health promotion, and illness prevention. It blends professional nursing practice with a health ministry, focusing on health and healing within the context of a Christian community. Parish nurses serve as health educators, referral sources,...
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Methods Of Healthcare Delivery System01:26

Methods Of Healthcare Delivery System

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At the different levels of the healthcare system, we see varying methods of healthcare used. These methods include managed care systems, case management, and primary healthcare.
Managed Care System:
The managed care system is designed to control the cost while maintaining the quality of care. The patient's care from admission to discharge is planned by the primary care provider or the case manager, also known as the gatekeeper. In a managed care system, the number of care providers is...
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Determining Soil-transmitted Helminth Infection Status and Physical Fitness of School-aged Children
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公共卫生 公共卫生

Samuel O Danso1,2, Ibrahim Alqatawneh3, Adewale Samuel Owo4

  • 1School of Computer Science and Engineering, University of Sunderland, Sunderland, England, United Kingdom.

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概括
此摘要是机器生成的。

这项研究引入了一个深度学习框架,用于在多样化的中年人群中早期检测阿尔茨海默病和相关痴呆症 (ADRD). 与长期短期记忆 (LSTM) 模型相比,卷积神经网络 (CNN) 模型在预测ADRD风险方面表现出更高的准确性.

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科学领域:

  • 人工智能的人工智能
  • 深度学习 (Deep Learning) 是一种深度学习.
  • 神经科学是一个神经科学.

背景情况:

  • 对于阿尔茨海默氏症和相关痴呆症 (ADRD) 的现有AI预测通常集中在特定的亚型和同质的老年人群上.
  • 这限制了人工智能工具对不同的人口统计和其他形式的痴呆症的适用性.
  • 这项研究提出了一个基于人工智能的深度学习框架,用于在异质的中年人群中早期检测ADRD.

研究的目的:

  • 开发和评估基于人工智能的深度学习框架,用于早期检测ADRD风险.
  • 评估卷积神经网络 (CNN) 和长期短期记忆 (LSTM) 模型在预测ADRD风险方面的性能.
  • 将框架应用于来自欧洲预防阿尔茨海默氏症痴呆症 (EPAD) 和预防痴呆症计划的多元群体.

主要方法:

  • 从EPAD和PREVENT数据集中整理了2796名没有痴呆症诊断的人群.
  • 个人被分为高,中和低风险组,基于ApoE4等位基因的存在和AD的家族病史.
  • 使用5倍交叉验证开发和优化了CNN和LSTM模型.

主要成果:

  • 协调的队列包括2796个人 (平均年龄62岁,年龄范围40-89岁;57.5%是女性;95%是白人).
  • 与LSTM模型相比,CNN模型实现了更高的准确性 (比LSTM模型高7个百分点) 和F1得分.
  • 在CNN模型中,接受器操作特征曲线 (AUROC) 下的平均面积得分 (97%对94%) 和较低的验证损失 (0.36对0.46) 均高于CNN模型.

结论:

  • 该CNN模型的卓越性能和低验证损失表明其可用于ADRD风险预测的概括性.
  • 该模型目前针对阿尔茨海默病 (AD) 进行了优化,但将使用转移学习来预测其他ADRD亚型.
  • 未来的工作将探索多式联运功能和可解释性,以增强CNN架构.