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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

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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...
439
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

Qiuran Rita Lyu1, Yanping Li2, Kevin Shen3

  • 1University of California, Berkeley, Berkeley, CA, USA.

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|December 23, 2025
PubMed
概括
此摘要是机器生成的。

这项研究介绍了ADLLM,一种先进的语言模型,可以使用多式数据准确预测阿尔茨海默病 (AD) 生物标志物和认知衰退. ADLLM对早期AD诊断和监测AD进展具有前途.

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科学领域:

  • 人工智能在医学中的应用
  • 计算神经科学是一种神经科学.
  • 生物标志物发现发现

背景情况:

  • 预测阿尔茨海默病 (AD) 的进展和个体生物标志物状态对于患者护理至关重要.
  • 高级语言模型 (LLM) 可以整合各种数据类型,以揭示AD中的复杂关系.

研究的目的:

  • 开发和验证ADLLM,这是一个增强的LLM,用于预测AD相关的结果.
  • 评估模型处理多式联运数据的能力,并提供可解释的预测.

主要方法:

  • 训练了ADLLM,一个基于LLaMA 3.1的LLM,使用LoRA进行微调,基于ADNI,HABS-HD和POINTER队伍的数据.
  • 利用人口统计,神经成像,液体生物标志物和临床评估作为Aβ/tau负担和MMSE变化的预测指标.
  • 采用了用于多式联接输入处理的文本提示,并使用AUC,准确性和特异性评估性能.

主要成果:

  • ADLLM在各队伍中取得了强大的预测性能,AUC对Aβ负担高达0.825和tau负担高达0.730.
  • 该模型准确地预测了MMSE变化率,AUC为0.806 (ADNI) 和0.816 (HABS-HD).
  • 注意力分析揭示了队列特定的变量重要性,突出了CSF生物标志物的ADNI和HABS-HD等离子体生物标志物的作用.

结论:

  • ADLLM有效地预测使用多模式输入的复合AD结果,并提供可解释的变量重要性.
  • 该模型在各个队列和任务中显示出一致的性能,表明其作为AD诊断和监测的可扩展临床工具的潜力.