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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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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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Classification of Illness01:17

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.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
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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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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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Ogive Graph01:07

Ogive Graph

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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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Related Experiment Video

Updated: Sep 17, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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MDP-GRL: Multi-disease Prediction by Graph-enabled Representation Learning.

Yongan Guo, Yeqi Huang, Yuao Wang

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    This study introduces MDP-GRL, a novel graph-based model for multi-label disease prediction using electronic health records (EHRs). MDP-GRL effectively addresses data complexity and imbalance, outperforming existing methods for accurate disease prediction.

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    Area of Science:

    • Medical Informatics
    • Machine Learning
    • Graph Neural Networks

    Background:

    • Electronic health records (EHRs) are crucial for automatic disease prediction.
    • Current methods struggle with complex medical data, including diverse disease relations, shared risk factors, data sparsity, and imbalance.
    • Existing approaches require enhancement to effectively utilize EHR features for predicting individual diseases.

    Purpose of the Study:

    • To propose MDP-GRL, a novel multi-label disease prediction model using graph-enabled representation learning.
    • To address challenges in EHR data, including complexity, sparsity, and imbalance.
    • To improve the accuracy and effectiveness of automatic disease prediction.

    Main Methods:

    • Constructing a medical knowledge graph (MKG) from patient and disease information in EHR.
    • Employing a graph neural network (GNN) for disease prediction.
    • Incorporating supplementary patient and disease data to combat sparsity.
    • Considering four relation patterns in MKG to handle data complexity.
    • Utilizing an attention mechanism and self-adversarial negative sampling to mitigate data imbalance.

    Main Results:

    • MDP-GRL demonstrates superior performance in multi-disease prediction compared to state-of-the-art methods.
    • The model effectively handles data sparsity by enriching patient and disease nodes.
    • The proposed methods successfully address data complexity and imbalance issues in EHR data.
    • Ablation studies confirm the effectiveness of individual components of MDP-GRL.

    Conclusions:

    • MDP-GRL offers a robust solution for multi-label disease prediction from EHRs.
    • The graph-based approach significantly enhances prediction accuracy by modeling complex relationships.
    • MDP-GRL provides a promising direction for advancing medical informatics and clinical decision support.