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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

12.0K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
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Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

385
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
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Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

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Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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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:
135

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Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
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使用追随者领先的集群算法 (FLCA) 进行集群分析的综合方法:图书识别分析.

Teng-Yun Cheng1, Sam Yu-Chieh Ho2, Tsair-Wei Chien3

  • 1Department of Emergency Medicine, Chi Mei Medical Center, Liouying, Tainan, Taiwan.

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概括

一个新的跟随领导者集群算法 (FLCA) 通过有效识别作者合作和研究主题来改进文献识别分析. 这种方法揭示了有和没有自我连接的聚类结果的显著差异,增强了研究发现.

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科学领域:

  • 图书识别和科学识别技术
  • 信息科学 信息科学
  • 网络分析 网络分析

背景情况:

  • 在定义作者合作,开发用于关键词分析的有效集群算法以及比较研究成果方面,图书计量学面临着挑战.
  • 现有的文献计量方法不足以支持识别有影响力的研究和向读者推相关文章.

研究的目的:

  • 为集群分析引入"跟随领导者集群算法" (FLCA).
  • 调查集群分析结果的差异,有和没有自我连接.
  • 展示FLCA在参考资料中的应用,用于分析作者合作和研究主题.

主要方法:

  • 从科学网络核心集合中搜索了JMIR医学信息学文章 (2016-2022年).
  • 应用FLCA算法来识别作者合作 (AC) 和主题.
  • 通过传统的文献计数和引用,比较了带有和没有自连接的集群结果,使用R表示可视化.

主要成果:

  • 在有自连接和没有自连接的分析之间观察到集群结果的显著差异 (53.8%的重叠).
  • 西大学 (韩国),格兰罗 (美国) 和特定的研究所在作者合作和主题方面领导了顶级集群.
  • 在JMIR医疗信息学出版物中,美国,西大学和格兰洛大学被确定为顶级实体.

结论:

  • FLCA算法为研究人员提供了一种强大的方法,以了解复杂的作者和关键字关系.
  • 对于未来的涉及作者合作的集群分析的文献计量研究,建议使用FLCA和R可视化.