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Diabetes Mellitus: Overview and Type I Subtype01:22

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Diabetes mellitus is a chronic metabolic disorder characterized by high blood glucose levels due to inadequate insulin production, insulin resistance, or both. The condition affects millions worldwide and can significantly impact their health and quality of life.
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Type 2 diabetes, characterized by insulin resistance, arises when the insulin receptors on cells lose responsiveness to insulin, diminishing the cell's capacity to take up glucose, resulting in elevated blood glucose levels. To receive a diagnosis of Type 2 diabetes, a series of blood glucose tests are necessary to assess whether the blood glucose falls within normal parameters. If the result is out of the normal range, a patient may be diagnosed as prediabetic or diabetic, depending on the...
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Pathophysiology of Diabetes01:20

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Diabetes mellitus is a chronic metabolic disorder characterized by hyperglycemia. The four categories of diabetes are type 1 diabetes, type 2 diabetes, other specific types of diabetes, and gestational diabetes.
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Diabetes: Symptoms, Diagnosis, and Complications01:15

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For most patients, experiencing several weeks of polyuria, polydipsia, fatigue, and significant weight loss may indicate the presence of diabetes. Furthermore, adults displaying the phenotypic appearance of type 2 diabetes (particularly those who are obese and not initially insulin-requiring), may have islet cell autoantibodies, suggesting autoimmune-mediated β cell destruction and a diagnosis of latent autoimmune diabetes of adults (LADA). The categorization of glucose homeostasis is...
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Time-Series Graph00:54

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A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
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In biostatistics, data are the observations collected for analysis. There are two main types: parametric and non-parametric. Parametric data, which include continuous (e.g., weight) and discrete numerical data (e.g., number of tablets), assume a particular distribution pattern, often the normal distribution. Non-parametric data do not adhere to a specific distribution and typically comprise nominal (e.g., gender) and ordinal categorical data (e.g., pain scale ratings).
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Marjorie: Visualizing Type 1 Diabetes Data to Support Pattern Exploration.

Anna Scimone, Klaus Eckelt, Marc Streit

    IEEE Transactions on Visualization and Computer Graphics
    |October 24, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Marjorie, a visual analytics tool, helps analyze diabetes data during appointments. It aids patients and diabetologists in understanding glucose patterns for better treatment decisions.

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    Area of Science:

    • Biomedical Informatics
    • Data Visualization
    • Diabetes Management

    Background:

    • Analyzing patient diabetes data during brief clinical appointments is challenging.
    • Effective visualization and pattern exploration are crucial for informed treatment decisions.

    Purpose of the Study:

    • To introduce Marjorie, a visual analytics approach for exploring diabetes patient data.
    • To support the identification of glucose profile patterns, including seasonal variations and mealtime fluctuations.

    Main Methods:

    • Marjorie combines visual and algorithmic methods for data exploration.
    • Utilizes modified horizon graphs and hierarchical clustering for glucose data representation.
    • Incorporates semantic zooming for multi-level temporal detail exploration.

    Main Results:

    • A case study demonstrated Marjorie's ability to provide insights into therapy and eating habits.
    • The tool supports joint exploration of diabetes data patterns by patients and diabetologists.
    • Potential for enabling more informed treatment decisions was highlighted.

    Conclusions:

    • Marjorie effectively supports the analysis of diabetes data in clinical settings.
    • The approach facilitates a deeper understanding of individual glucose patterns.
    • Enhanced data exploration can lead to improved diabetes care and patient outcomes.