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相关概念视频

Language01:16

Language

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Language is a unique communication system that uses words and systematic rules to organize and transmit information. Unlike other forms of communication, which may involve postures, movements, odors, or vocalizations, language relies on symbols and grammar. This makes human communication distinct from that of other species, who also communicate but do not use language in the same way humans do.
Corballis and Suddendorf (2007) and Tomasello and Rakoczy (2003) highlight the role of language in...
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Review and Preview01:10

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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
Percentiles are a type of fractile that partition data into...
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Review and Preview01:13

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Data are individual items of information obtained from a population or sample. Data may be classified as qualitative (categorical), quantitative continuous, or quantitative discrete. Because it is not practical to measure the entire population in a study, researchers use samples to represent the population. A random sample is a representative group from the population chosen by using a method that gives each individual in the population an equal chance of being included in the sample. Random...
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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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Components of Language01:24

Components of Language

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Language, whether spoken, signed, or written, consists of specific components: lexicon and grammar. The lexicon is the vocabulary of a language, comprising its words. Grammar is the set of rules used to convey meaning through the lexicon. For example, English grammar adds “-ed” to most verbs to indicate past tense. Words are formed by combining phonemes, which are the basic sound units of a language. Different languages have different sets of phonemes (e.g., “ah” vs.
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相关实验视频

Updated: Feb 14, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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心血管预防中的大型语言模型:叙述性审查和治理框架

José Ferreira Santos1,2, Hélder Dores3,4,5,6

  • 1Católica Medical School, Sintra Campus, Estrada Octávio Pato, 2635-631 Rio de Mouro, Portugal.

Diagnostics (Basel, Switzerland)
|February 13, 2026
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概括

大型语言模型 (LLM) 在心血管预防方面显示出有前途的潜力,用于患者教育和系统应用. 然而,它们需要监督临床使用,作为推理引擎来支持,而不是取代医疗保健专业人员.

关键词:
人工智能的人工智能是人工智能.预防心血管疾病临床决策支持 临床决策支持大型语言模型.风险分层的分层化

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科学领域:

  • 人工智能在医学中的应用
  • 预防心血管疾病 预防心血管疾病
  • 临床信息学 临床信息学

背景情况:

  • 大型语言模型 (LLM) 在医疗保健中越来越多地使用.
  • 它们在心血管 (CV) 预防中的特殊作用需要澄清.
  • 本综述探讨了预防性心脏病学中的LLM应用.

研究的目的:

  • 综合LLM在预防性心脏病学中的应用的证据.
  • 为安全的LLM实施提出一个治理框架.
  • 评估LLM在心血管预防方面的潜力和局限性.

主要方法:

  • 文学综合叙事综述 (2015年1月 - 2025年11月).
  • 跨患者,临床医生和系统应用领域的综合.
  • 对健康素养,决策支持和数据管理的证据评估.

主要成果:

  • LLM提供了同情的患者教育,但缺乏无监督咨询的细微差别.
  • 临床医生支持包括笔记总结和文档起草;风险计算不可靠.
  • 系统应用程序显示了表型和风险预测的潜力,但面临着幻觉和数据隐私等挑战.

结论:

  • 在CV预防方面,LLM可以解决结构性障碍.
  • 目前的部署应该是监督推理引擎,增强临床医生的判断力.
  • 这就是C.A.R.D.I.O.的C.A.R.D.I.O. 为负责任地将LLM纳入临床实践提出了框架.