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: Symptoms, Diagnosis, and Complications01:15

Diabetes: Symptoms, Diagnosis, and Complications

2.7K
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...
2.7K
Survival Tree01:19

Survival Tree

468
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...
468
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

956
Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time...
956
Diabetes Mellitus: Type 2 and Gestational01:22

Diabetes Mellitus: Type 2 and Gestational

5.5K
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...
5.5K

You might also read

Related Articles

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

Sort by
Same author

Six- and Twelve-Month Changes in Body Composition and 24-h Energy Expenditure After a Very Low-Calorie Ketogenic Diet.

Obesity (Silver Spring, Md.)·2026
Same author

Evaluating large language models for structuring cardiology reports: a real-world clinical study on patient subtyping and trial recruitment.

International journal of medical informatics·2026
Same author

ViTMARE - A Vision Transformer Pipeline for Anomaly Detection in 3D Brain MRI.

Studies in health technology and informatics·2026
Same author

Environmental Personal Exposure Clusters to Investigate Multiple Sclerosis and Amyotrophic Lateral Sclerosis Progression.

Studies in health technology and informatics·2026
Same author

Sub-Phenotyping of Pediatric Celiac Disease with Topological Data Analysis.

Studies in health technology and informatics·2026
Same author

Machine Learning Prediction of Growth Hormone Response in Children Non-Growth Hormone-Deficient Short Stature.

Studies in health technology and informatics·2026

Related Experiment Video

Updated: Mar 27, 2026

Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression
04:36

Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression

Published on: January 12, 2024

1.7K

Improving risk-stratification of Diabetes complications using temporal data mining.

Lucia Sacchi, Arianna Dagliati, Daniele Segagni

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 7, 2016
    PubMed
    Summary

    Temporal data mining of drug purchases helps stratify patients with Type 2 Diabetes (T2D). This approach identifies purchasing patterns that correlate with clinical conditions, improving disease management and risk stratification for better patient outcomes.

    More Related Videos

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
    06:55

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

    Published on: January 8, 2020

    15.5K
    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

    8.2K

    Related Experiment Videos

    Last Updated: Mar 27, 2026

    Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression
    04:36

    Author Spotlight: Understanding Retinal Vessel Resilience and Disease Progression

    Published on: January 12, 2024

    1.7K
    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
    06:55

    Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

    Published on: January 8, 2020

    15.5K
    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

    8.2K

    Area of Science:

    • Health Informatics
    • Data Mining
    • Diabetes Management

    Background:

    • Effective management of Type 2 Diabetes (T2D) requires understanding factors that worsen disease control.
    • Continuous patient stratification is essential for providing decision support in chronic T2D care.

    Purpose of the Study:

    • To demonstrate the utility of temporal data mining for enhancing risk stratification in T2D patients.
    • To integrate heterogeneous data sources for improved T2D patient management.

    Main Methods:

    • Utilized administrative data on drug purchases for patient grouping.
    • Applied temporal data mining techniques to detect drug consumption patterns.
    • Merged drug purchasing patterns with clinical data for analysis.

    Main Results:

    • Successfully stratified patients into meaningful groups based on purchasing attitudes.
    • Identified significant differences in clinical conditions among the identified patient groups.
    • Demonstrated the relevance of data mining methods in explaining clinical status.

    Conclusions:

    • Temporal data mining of administrative drug purchase data is effective for patient stratification in T2D.
    • This approach provides valuable insights into clinical conditions by analyzing purchasing patterns.
    • Integrating administrative and clinical data enhances decision support for chronic T2D management.