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Related Concept Videos

Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

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...
Type II Diabetes I: Introduction01:26

Type II Diabetes I: Introduction

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by insulin resistance, in which target tissues such as the liver, muscle, and adipose tissue respond poorly to insulin. It is also associated with inadequate compensatory insulin secretion, where pancreatic β-cells fail to produce sufficient insulin. Together, these abnormalities lead to persistent hyperglycemia.EtiologyT2DM develops through a complex interaction of genetic predisposition and environmental or...
Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis01:25

Type II Diabetes Mellitus III: Clinical Manifestations and Diagnosis

Type 2 diabetes mellitus develops gradually and is often asymptomatic in early stages.Clinical ManifestationsWhen symptoms appear, they include fatigue, blurred vision, pruritus, delayed wound healing, and recurrent infections, particularly candidal infections. Peripheral neuropathy may present as numbness or tingling in the extremities. Classic hyperglycemia symptoms—polyuria, polydipsia, and polyphagia—are less common. Most patients are overweight and frequently have associated hypertension...
Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

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.
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
Type II Diabetes II: Pathophysiology01:24

Type II Diabetes II: Pathophysiology

PathophysiologyType 2 diabetes mellitus (T2DM ) is a chronic metabolic disorder characterized by insulin resistance and progressive pancreatic β-cell dysfunction, leading to impaired glucose homeostasis. It results from interactions among genetic predisposition, environmental factors, and metabolic stressors, such as overnutrition and a sedentary lifestyle.Insulin Resistance and Glucose DysregulationEarly T2DM involves insulin resistance in skeletal muscle, adipose tissue, and the liver.
Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...

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Updated: May 31, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Identifying type 1 and type 2 diabetic cases using administrative data: a tree-structured model.

Weihsuan Lo-Ciganic1, Janice C Zgibor, Kristine Ruppert

  • 1Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, Pennsylvania, USA. wel32@pitt.edu

Journal of Diabetes Science and Technology
|July 5, 2011
PubMed
Summary
This summary is machine-generated.

A new classification tree model accurately distinguishes type 1 diabetes mellitus (T1DM) from type 2 diabetes mellitus (T2DM) in administrative data. This advance enables large-scale, subtype-specific analyses of diabetes mellitus (DM) patient outcomes.

Related Experiment Videos

Last Updated: May 31, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Medical Informatics
  • Epidemiology
  • Biostatistics

Background:

  • Administrative diabetes mellitus (DM) registries often lack differentiation between type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM).
  • Accurate subtype classification is crucial for targeted research and patient care.

Purpose of the Study:

  • To develop and validate a prediction rule for distinguishing T1DM from T2DM within a large administrative database.
  • To leverage a classification tree model for improved DM subtyping.

Main Methods:

  • Utilized administrative and clinical data from 209,647 DM patients (2000-2009).
  • Applied nonparametric classification tree models with 10-fold cross-validation.
  • Estimated sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for T1DM prediction.

Main Results:

  • Key predictors for T1DM included age <40, specific ICD-9 codes, inpatient insulin use, and diabetic ketoacidosis episodes.
  • The tree-structured model achieved 92.8% sensitivity, 99.3% specificity, 89.5% PPV, and 99.5% NPV.
  • Performance compared favorably to a complex clinical algorithm.

Conclusions:

  • A preliminary predictive rule effectively distinguishes T1DM from T2DM in administrative data.
  • This method facilitates large-scale, subtype-specific analyses of DM-related costs, morbidity, and mortality.