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 Experiment Videos

Hypoglycemia prediction and detection using optimal estimation.

Cesar C Palerm1, John P Willis, James Desemone

  • 1Department of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180-3590, USA.

Diabetes Technology & Therapeutics
|March 2, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Smart manufacturing for continuous downstream processing of monoclonal antibodies at the lab-scale.

SLAS technology·2026
Same author

Mediastinal Germ Cell Tumors: Up Close and Personal.

JMIR cancer·2026
Same author

Process Systems Engineering in Precision Medicine: Opportunities in Autologous CAR-T Therapy.

Engineering in life sciences·2026
Same author

Research Code Sharing in Support of Gold Standard Science.

Journal of diabetes science and technology·2026
Same author

Probabilistic Meal Detection and Estimation in Type 1 Diabetes With Extreme Shape Variability.

Journal of diabetes science and technology·2025
Same author

Metabolic Models, in Silico Trials, and Algorithms.

Journal of diabetes science and technology·2025
Same journal

Continuous Glucose Monitoring Metrics and Perinatal Outcomes in Pregnant Women with Type 2 Diabetes.

Diabetes technology & therapeutics·2026
Same journal

Invited Commentary: Using Large Clinical Databases.

Diabetes technology & therapeutics·2026
Same journal

Impact of Technology Introduction on Glycemic Control of People Living with Type 1 Diabetes in Belgium: An Observational, Real-World Study.

Diabetes technology & therapeutics·2026
Same journal

Differences in Achieving Stringent Glycemic Targets Among Youth with Type 1 Diabetes: A SWEET Registry Study.

Diabetes technology & therapeutics·2026
Same journal

<i>Letter:</i> "Nocturnal Hypoglycemia: Characterization with Continuous Glucose Monitoring in a Real-World Setting".

Diabetes technology & therapeutics·2026
Same journal

Transient Benign Ketosis Is Common in Type 1 Diabetes During Very-Low Carbohydrate States: Novel Data from Continuous Ketone Sensor.

Diabetes technology & therapeutics·2026
See all related articles

Continuous glucose monitoring (CGM) can predict hypoglycemia in diabetes patients. This study optimizes prediction models by adjusting sampling frequency and alarm thresholds to minimize missed events and false alarms.

Area of Science:

  • Endocrinology
  • Biomedical Engineering
  • Medical Informatics

Background:

  • Diabetes management involves balancing glycemic control to prevent long-term complications with the risk of iatrogenic hypoglycemia.
  • Hypoglycemia unawareness exacerbates the risks associated with low blood glucose levels.
  • Continuous glucose monitoring (CGM) systems offer potential for detecting and predicting hypoglycemic episodes.

Purpose of the Study:

  • To investigate the impact of measurement sampling frequency, threshold levels, and prediction horizon on the accuracy of hypoglycemia prediction using optimal estimation theory.
  • To explore methods for tuning prediction algorithms to balance sensitivity and specificity, thereby reducing false alarms and missed hypoglycemic events.

Main Methods:

  • Application of optimal estimation theory to develop a hypoglycemia prediction model.

Related Experiment Videos

  • Analysis of the influence of key parameters: measurement sampling frequency, threshold level, and prediction horizon.
  • Evaluation of prediction performance in terms of sensitivity and specificity.

    • Main Results:

    • Demonstrated the effect of sampling frequency, threshold, and prediction horizon on prediction sensitivity and specificity.
    • Showcased how optimal estimators can be tuned to manage the trade-off between false alarm rates and missed hypoglycemic episodes.
    • Identified optimal parameter settings for effective hypoglycemia prediction.

    Conclusions:

    • CGM-based hypoglycemia prediction models can be optimized to enhance patient safety in diabetes management.
    • Tuning prediction parameters allows for a personalized approach to minimizing hypoglycemia risks.
    • Future recommendations include utilizing dynamic alarm levels based on glucose trends and prediction horizons.