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相关概念视频

Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
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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:
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
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Prediction Intervals01:03

Prediction Intervals

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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
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Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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相关实验视频

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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通过单一值分解和核回归来进行多步骤的流感预测,使用MARCOS引导的基于梯度的优化.

Guo Hongliang1, Zhang Zhiyao1, Iman Ahmadianfar2

  • 1College of Information Technology, Jilin Agricultural University, Changchun, 130118, China.

Computers in biology and medicine
|December 29, 2023
PubMed
概括

准确的流感预测对公共卫生至关重要. 一个新的混合模型,MVMD-H-SKRR-GBO,显著改善了每周流感样疾病 (ILI) 率的预测.

关键词:
在GBO算法中,GBO算法预测流感的预测核心脊回归的回归方法马可斯方法的方法在前面的多个步骤.单数值分解 (SVD) 是指单数值的分解.

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科学领域:

  • 公共卫生 公共卫生
  • 流行病学 流行病学
  • 计算科学 计算科学

背景情况:

  • 流感疫情对公众健康构成重大挑战.
  • 准确预测类似流感的疾病 (ILI) 率对于有效的公共卫生干预至关重要.
  • 现有的模型往往难以应对ILI数据的复杂性和非静止性.

研究的目的:

  • 开发和验证一种新的混合机器学习模型,以改进每周ILI利率的多步预测.
  • 提高在中国南部和北部流感疫情预测的准确性.
  • 为了解决ILI时间序列数据固有的非静止性和复杂性.

主要方法:

  • 开发了一个混合模型 (MVMD-H-SKRR-GBO),结合了多变量变化模式分解 (MVMD),与内核回归 (SKRR) 的单值分解和基于梯度的优化 (GBO).
  • 使用XGBoost进行特征选择,以确定最佳的前体信息.
  • 该模型分解了ILI信号,结合了滞后的组件,并汇总了4周和7周前期预测的预测.
  • 模型性能与深度随机向量功能链接 (dRVFL),Ridge回归和封闭循环单元神经网络 (GRU) 模型相对验证,使用MARCOS多标准决策方法.

主要成果:

  • 与其他基准模型相比,MVMD-H-SKRR-GBO模型在预测每周ILI利率方面表现出卓越的准确性.
  • 关键绩效指标包括R=0.946,RMSE=0.388,IA=0.970和U95%=1.075在t + 7时间范围内.
  • 该研究通过信号分解技术成功地解决了数据的非静止性和复杂性.

结论:

  • 开发的MVMD-H-SKRR-GBO模型提供了一个高度准确和强大的方法,用于对ILI利率的多步预测.
  • 这种先进的预测能力可以显著帮助公共卫生规划和应对流感疫情.
  • 混合机器学习范式为流行病学时间序列预测提供了一个有希望的方向.