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Related Concept Videos

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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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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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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A Mobile App That Addresses Interpretability Challenges in Machine Learning-Based Diabetes Predictions: Survey-Based

Rasha Hendawi1, Juan Li1, Souradip Roy1

  • 1North Dakota State University, Fargo, ND, United States.

JMIR Formative Research
|November 13, 2023
PubMed
Summary

Explainable AI (XAI) for diabetes care improves understanding and trust. The XAI4Diabetes platform enhances healthcare professionals' comprehension of AI predictions and their underlying reasoning.

Keywords:
artificial intelligencediabetesdisease predictionexplainable AIknowledge graphmachine learningontology

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning for Disease Prediction
  • Explainable AI (XAI)

Background:

  • Machine learning (ML) models are effective for diabetes diagnosis and prediction but often function as "black boxes."
  • The opacity of ML models hinders adoption in healthcare due to lack of trust and understanding.
  • This lack of transparency is a significant barrier to integrating AI into diabetes care.

Purpose of the Study:

  • To develop and evaluate XAI4Diabetes, an explainable AI platform for diabetes care.
  • To empower healthcare professionals with interpretable AI predictions and recommendations.
  • To enhance understanding of complex ML models and their outcomes in diabetes risk assessment.

Main Methods:

  • Developed a multimodule explanation framework (XAI4Diabetes) using ML, knowledge graphs, and ontologies.
  • The platform includes modules for knowledge base, knowledge matching, prediction, and interpretation.
  • Assessed usability and impact on comprehension via a survey-based user study with medical professionals.

Main Results:

  • A prototype mobile app was developed and evaluated through usability studies and satisfaction surveys.
  • Medical professionals showed improved understanding of the diabetes prediction process, data sets, features, and feature significance.
  • Participants reported increased trust and understanding of AI predictions after using XAI4Diabetes, with high overall satisfaction.

Conclusions:

  • XAI4Diabetes is a versatile platform for explainable diabetes risk prediction, enhancing transparency.
  • The platform empowers healthcare professionals to understand AI decision-making, fostering trust and mitigating bias.
  • XAI4Diabetes facilitates broader AI integration into diabetes care by addressing the need for interpretability.