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相关概念视频

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
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Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
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Pharmacokinetic Models: Comparison and Selection Criterion01:26

Pharmacokinetic Models: Comparison and Selection Criterion

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Physiological and compartmental models are valuable tools used in studying biological systems. These models rely on differential equations to maintain mass balance within the system, ensuring an accurate representation of the dynamic processes at play.
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
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Combination Therapies and Personalized Medicine02:50

Combination Therapies and Personalized Medicine

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
The combination of the drug acetazolamide and sulforaphane is a good example of combination therapy to treat cancer. The cells in the interior of a large tumor often die due to the hypoxic and...
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Nursing Clinical Information System01:27

Nursing Clinical Information System

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Nursing Clinical Information System (NCIS)
A Nursing Clinical Information System (NCIS) is a specialized type of healthcare information system tailored to meet the unique needs of nursing practice. It incorporates the principles of nursing informatics to streamline information management and improve the quality of care delivery.
Critical attributes of NCIS include:
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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通过两阶段推系统改善临床决策.

Shaina Raza, Chen Ding

    IEEE/ACM transactions on computational biology and bioinformatics
    |September 22, 2023
    PubMed
    概括

    这项研究引入了一个新的两阶段推框架,以加强临床决策. 该系统有效地从电子病历中提取和排列患者数据,提高医疗保健提供者的推准确性.

    科学领域:

    • 医疗信息学 医疗信息学
    • 医疗保健中的人工智能
    • 临床决策支持系统 临床决策支持系统

    背景情况:

    • 临床决策对于医疗保健从业者来说是复杂而耗时的.
    • 现有的临床推系统 (RS) 面临挑战,原因是多方面的临床数据和需要个性化咨询.
    • 电子健康记录 (EHR) 包含大量的数据,这些数据对于明智的临床决策至关重要.

    研究的目的:

    • 引入一个新的两阶段推框架,以协助医疗保健从业人员在临床决策中.
    • 为了开发和验证框架,利用公开可访问的电子健康记录数据集.
    • 提高个人推诊断,药物和处方的准确性和相关性.

    主要方法:

    • 一个两阶段的框架,结合了深度神经网络检索器和深度学习排名器,两者都基于预训练的变压器模型.
    • 阶段1:使用深度神经网络从电子健康记录中提取候选项目 (诊断,药物,处方).
    • 第二阶段:使用深度学习模型对医疗保健提供者来说最相关的项目进行排名和确定.

    主要成果:

    • 拟议的两阶段推框架实现了与第二好的基线模型相比,大约12.3%的宏观平均F1的绩效增长.
    • 使用各种评估指标的验证证明了该模型的卓越性能.

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  • 定性分析证实了该框架在多个维度上的高性能.
  • 结论:

    • 两阶段推框架通过提高个性化推的准确性,为临床决策支持提供了显著的改进.
    • 该研究强调了深度学习和转换器模型在处理复杂的EHR数据的临床应用中的潜力.
    • 未来的研究应该解决数据可用性和隐私问题等挑战,以进一步推进临床推系统.