Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

2.4K
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...
2.4K
Diabetes Mellitus: Overview and Type I Subtype01:22

Diabetes Mellitus: Overview and Type I Subtype

2.7K
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...
2.7K
Diabetes: Symptoms, Diagnosis, and Complications01:15

Diabetes: Symptoms, Diagnosis, and Complications

554
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...
554
Carbohydrate Metabolism01:36

Carbohydrate Metabolism

11.2K
Carbohydrates are polymers composed of molecules containing atoms of carbon, hydrogen and oxygen. One gram of carbohydrate can provide four kilo-calories of energy, which makes it the most efficient instant energy source.
Starch accounts for approximately 60% of the carbohydrates consumed by humans. Since amylase enzymes cannot function in the stomach's acidic environment, starch can only be digested in the mouth and small intestine. Simple sugars are found naturally in milk and fruits in...
11.2K
Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

130
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
130
Insulin: Dosing Regimen and Adverse Effects01:16

Insulin: Dosing Regimen and Adverse Effects

179
Insulin-replacement therapy usually includes both long-acting insulin (basal) and short-acting insulin (to cater to postprandial needs). In a diverse group of type 1 diabetes patients, the average daily insulin dose is typically 0.5-0.7 units/kg body weight. However, obese patients and pubertal adolescents may need more due to insulin resistance.
The basal dose constitutes about 40%-50% of the total daily dose, with the rest as premeal insulin. The mealtime insulin dose should mirror...
179

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

ACCELERATE-BASSO: early experiences and emerging best practices for ontology development in behavioral and social science research.

Journal of biomedical semantics·2026
Same author

Develop and validate a fair machine learning model to identify patients with high data-continuity in electronic health records data.

JAMIA open·2026
Same author

Build fair machine learning models to predict adverse outcomes for heart failure patients with preserved ejection fraction and with reduced ejection fraction.

JAMIA open·2026
Same author

Geographic variation in eligibility and uptake of GLP-1 receptor agonists for obesity in US adults.

American journal of preventive cardiology·2026
Same author

Histone H3K18 lactylation: An exercise-induced epigenetic mechanism that inhibits osteoclast activity and protects against osteoporosis.

Journal of orthopaedic translation·2026
Same author

Methodological Approaches to Real-World Evidence Generation for Glucagon-like Peptide-1-Based Therapies: Synopsis of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop.

Annals of internal medicine·2026

Related Experiment Video

Updated: Jul 8, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K

A Fair Individualized Polysocial Risk Score for Identifying Increased Social Risk in Type 2 Diabetes.

Yu Huang1, Jingchuan Guo2, William T Donahoo3

  • 1Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, USA.

Research Square
|December 18, 2023
PubMed
Summary

A new machine learning model, the individualized polysocial risk score (iPsRS), can fairly identify patients with type 2 diabetes (T2D) at high risk for hospitalization due to social determinants of health (SDoH). This tool helps manage social risks at the point of care.

Keywords:
FairnessMachine LearningMachine learningPredictionType 2 diabetes

More Related Videos

Author Spotlight: Unveiling Mechanisms of Stress Resilience - Significant Findings, Advancements, and Future Research
05:03

Author Spotlight: Unveiling Mechanisms of Stress Resilience - Significant Findings, Advancements, and Future Research

Published on: December 15, 2023

4.1K
A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
06:46

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19

Published on: July 5, 2022

2.8K

Related Experiment Videos

Last Updated: Jul 8, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
07:31

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack

Published on: May 15, 2020

7.1K
Author Spotlight: Unveiling Mechanisms of Stress Resilience - Significant Findings, Advancements, and Future Research
05:03

Author Spotlight: Unveiling Mechanisms of Stress Resilience - Significant Findings, Advancements, and Future Research

Published on: December 15, 2023

4.1K
A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19
06:46

A High-Throughput Multiplexed Screening for Type 1 Diabetes, Celiac Diseases, and COVID-19

Published on: July 5, 2022

2.8K

Area of Science:

  • Health Informatics
  • Machine Learning in Healthcare
  • Social Determinants of Health Research

Background:

  • Racial and ethnic minority groups disproportionately experience type 2 diabetes (T2D) and its complications, often linked to social determinants of health (SDoH).
  • Effective management of social risks at the point of care is crucial for mitigating these health disparities.

Approach:

  • Developed an electronic health records (EHR)-based machine learning (ML) pipeline, the individualized polysocial risk score (iPsRS), to predict hospitalization risk in T2D patients.
  • Incorporated both contextual (e.g., neighborhood deprivation) and individual-level (e.g., housing instability) SDoH data for comprehensive risk assessment.
  • Utilized explainable AI (XAI) and fairness optimization to ensure equitable performance across diverse patient populations.

Key Points:

  • The iPsRS model, trained on 10,192 T2D patients, achieved a C statistic of 0.72 in predicting 1-year hospitalizations after fairness optimization.
  • The model demonstrated strong utility, with the top 5% of iPsRS identifying individuals with a 28.1% 1-year hospitalization rate, approximately 13 times higher than the bottom decile.
  • Fairness optimization ensured the iPsRS performed equitably across different racial and ethnic groups.

Conclusions:

  • The iPsRS ML pipeline offers a fair and accurate method for screening T2D patients at increased risk of hospitalization due to SDoH.
  • This approach facilitates targeted interventions for high-risk individuals, addressing unmet social needs within real-world clinical settings.