Federal Learning Algorithm for an Intelligent Insulin Decision System for Dynamic Glucose Control in Type 2 Diabetic Patients
July 14, 2024 updated by: Shanghai Zhongshan Hospital
A Multicenter Federal Learning Algorithm to Build an Intelligent Insulin Decision System for Dynamic Glucose Control in Type 2 Diabetic Patients
Constructing an intelligent insulin decision-making system for dynamic glucose control in type 2 diabetes mellitus via a multicentre federated learning algorithm, comparing the performance of the federated learning model, the local model and the initial model, and evaluating their feasibility and safety.
Study Overview
Detailed Description
Constructing an intelligent insulin decision-making system for dynamic glucose control in type 2 diabetes mellitus via a multicentre federated learning algorithm, comparing the performance of the federated learning model, the local model and the initial model, and evaluating their feasibility and safety.
Study Type
Observational
Enrollment (Estimated)
30100
Contacts and Locations
This section provides the contact details for those conducting the study, and information on where this study is being conducted.
Study Contact
- Name: Xiaoying Li, PhD.
- Phone Number: 02164041990
- Email: li.xiaoying@zs-hospital.sh.cn
Study Contact Backup
- Name: Ying Chen
- Phone Number: 13482
- Email: chen.ying4@zs-hospital.sh.cn
Participation Criteria
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
No
Sampling Method
Non-Probability Sample
Study Population
type 2 diabetes who
Description
Inclusion Criteria:
- type 2 diabetes inpatients receiving insulin therapy
Exclusion Criteria:
- use of insulin pumps or glucocorticoids during hospitalisation
- less than two days of insulin therapy
Study Plan
This section provides details of the study plan, including how the study is designed and what the study is measuring.
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
patients record
|
using patient record to construct AI models
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Time Frame |
|---|---|
|
the accuracy of AI models
Time Frame: up to 2 years
|
up to 2 years
|
Collaborators and Investigators
This is where you will find people and organizations involved with this study.
Sponsor
Investigators
- Study Director: Xiaoying Li, Professor, Fudan University
Study record dates
These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.
Study Major Dates
Study Start (Estimated)
September 1, 2024
Primary Completion (Estimated)
June 1, 2026
Study Completion (Estimated)
June 30, 2026
Study Registration Dates
First Submitted
May 23, 2024
First Submitted That Met QC Criteria
May 29, 2024
First Posted (Actual)
May 30, 2024
Study Record Updates
Last Update Posted (Actual)
July 16, 2024
Last Update Submitted That Met QC Criteria
July 14, 2024
Last Verified
May 1, 2024
More Information
Terms related to this study
Other Study ID Numbers
- 20240324025304420
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
UNDECIDED
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Yes
Studies a U.S. FDA-regulated device product
No
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.