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相关概念视频

Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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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.
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Classification of Illness

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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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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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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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An ogive graph is sometimes called a cumulative frequency polygon. It is one type of frequency polygon that shows cumulative frequency. In other words, the cumulative percentages are added to the graph from left to right. An ogive graph plots cumulative frequency on the vertical y-axis and class boundaries along the horizontal x-axis. It’s very similar to a histogram; only instead of rectangles, an ogive displays a single point where the top right of the rectangle would be. Creating this...
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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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MDP-GRL:通过图形支持的表示学习来预测多种疾病.

Yongan Guo, Yeqi Huang, Yuao Wang

    IEEE journal of biomedical and health informatics
    |June 30, 2025
    PubMed
    概括

    本研究介绍了MDP-GRL,这是一种基于图形的新型模型,用于使用电子健康记录 (EHR) 进行多标签疾病预测. MDP-GRL有效地解决了数据的复杂性和不平衡性,超过了准确预测疾病的现有方法.

    科学领域:

    • 医疗信息学 医疗信息学
    • 机器学习 机器学习
    • 图形神经网络的神经网络

    背景情况:

    • 电子健康记录 (EHR) 对于自动预测疾病至关重要.
    • 目前的方法与复杂的医疗数据作斗争,包括多种疾病关系,共同的风险因素,数据稀疏性和不平衡.
    • 现有的方法需要改进,以有效地利用电子健康记录功能来预测单个疾病.

    研究的目的:

    • 提出MDP-GRL,一种使用图形支持的表示学习的新型多标签疾病预测模型.
    • 解决电子健康数据的挑战,包括复杂性,稀疏性和不平衡性.
    • 提高自动疾病预测的准确性和有效性.

    主要方法:

    • 从EHR中的患者和疾病信息构建医学知识图 (MKG).
    • 使用图形神经网络 (GNN) 进行疾病预测.
    • 纳入补充患者和疾病数据以打击稀疏性.
    • 考虑MKG中的四种关系模式来处理数据的复杂性.
    • 使用注意力机制和自我对立的负采样来缓解数据不平衡.

    主要成果:

    • 与最先进的方法相比,MDP-GRL在多种疾病预测方面表现优越.

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  • 该模型通过丰富患者和疾病节点来有效处理数据稀疏性.
  • 提出的方法成功地解决了EHR数据中的数据复杂性和不平衡问题.
  • 废弃性研究证实了MDP-GRL的单个成分的有效性.

    • 结论:

    • MDP-GRL提供了一个可靠的解决方案,用于从EHR中预测多标签疾病.
    • 基于图形的方法通过建模复杂的关系,显著提高了预测准确性.
    • MDP-GRL为推进医疗信息学和临床决策支持提供了一个有前途的方向.