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相关概念视频

Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

131
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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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
56
Language Development01:22

Language Development

368
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
368

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通过自然语言处理和概率语言模型进行可扩展的事件检测.

Colin G Walsh, Drew Wilimitis, Qingxia Chen

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    这项研究引入了一种新方法,使用临床笔记上的自然语言处理 (NLP) 来识别患者事件,如自杀未遂和睡眠行为,改进了超越结构化数据的安全监测.

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    科学领域:

    • 医疗信息学 医疗信息学
    • 临床信息学 临床信息学
    • 在医疗保健中的自然语言处理.

    背景情况:

    • 营销后安全监测对于大规模检测临床事件至关重要.
    • 电子健康记录 (EHR) 中的结构化数据 (例如诊断代码) 可能对监控不准确.
    • 非结构化的临床文本提供了更丰富的数据来源,但需要先进的分析.

    研究的目的:

    • 开发和验证一种使用非结构化临床文本数据的新型事件表型方法.
    • 为了证明这种方法在不同临床表型的普遍性.
    • 评估表型化方法的性能,包括潜在的种族差异.

    主要方法:

    • 开发了一种基于验证方法 (PheRe) 的新型表型方法,用于分析非结构化EHR数据.
    • 用大型数据集验证了两种表型的方法:自杀企图和与睡眠有关的行为.
    • 根据银标准 (诊断编码) 和黄金标准 (手动图表审查) 进行验证.

    主要成果:

    • 为了自杀企图表型化,实现了精度回忆曲线 (AUPR) 下的面积为0.77.
    • 观察到睡眠相关行为表型的AUPR为~0.31.
    • 根据编码的种族确定了性能变化,表明需要对算法保持警和缓解.

    结论:

    • 开发的NLP方法有效地利用非结构化的临床文本进行可扩展的事件表型化.
    • 该方法有望通过克服结构化数据的局限性来加强营销后安全监测.
    • 需要进一步的工作来解决算法偏见,并确保在多样化的患者群体中提供公平的表现.