Clinical Research
A resource to get the latest clinical evidence, studies, models and frameworks to advance knowledge of how best to manage chronic health conditions.
Welldoc is committed to our scientific research, advancing digital health, transforming chronic care, and driving value across healthcare. Areas of focus include digital health engagement, advancing artificial intelligence, cardiometabolic condition outcomes, cost and value, and real-world integration into the health ecosystem.
Behavioral Factors, Empowerment Bolsters Self-Management
The increasing use of web-based or technology-enabled solutions for health management presents opportunities to improve patient self-management…A Novel Approach to Continuous Glucose Monitoring
Health Plan Opportunities to Control Costs Related to Chronic Disease
Moving the Dial in Lowering and Controlling A1C
The Power of Integrated Peer Support and Digital Health
Topics
Type
Methods, Analysis, and Insights from a State-Of-The-Art Large Glucose Model
Here, Welldoc builds upon our prior AI models that used CGM data only and expands to a new Large Glucose Model (LGM), which uses both CGM values and time series inputs to predict glucose trajectories at 30mins, 60mins and 2-hour time horizons. Results were analyzed across different Type 1 and Type 2 diabetes population subgroups (time of day, age group and total engagement levels) within a mobile diabetes management application.
This work will allow Welldoc to power new cardiometabolic focused capabilities and innovations in enhanced AI-driven personalization. Welldoc continues to drive this type of research to develop novel solutions leveraging data from real-world sensors, like CGM, and provide deep insights into subgroup level patterns and differences.
Junjie Luo, Abhimanyu Kumbara, Anand K. Iyer, Mansur E. Shomali, and Guodong “Gordon” Gao
Evaluating Perplexity and Glucose Level Impact on State-Of-The-Art Generative Pre-trained Transformer (GPT) Model to Predict Glucose Values at Different Time Intervals
Junjie Luo, Abhimanyu Kumbara, Anand K. Iyer, Mansur E. Shomali, and Guodong “Gordon” Gao
Nutritional Analysis and Advanced Artificial Intelligence (AI) Predicts Weight Loss for People with Diabetes
Catherine Brown, MS, RD, Anand Iyer, PhD, MBA, Abhimanyu Kumbara, MS, MBA, Maxwell Ebert, MPH
The Critical Elements of Digital Health in Diabetes and Cardiometabolic Care
Mansur Shomali, Pablo Mora, Grazia Aleppo, Malinda Peeples, Abhimanyu Kumbara, Janice MacLeod, Anand Iyer
Evaluating a State-of-the-art Generative Pre-trained Transformer Model to Predict Continuous Glucose Monitoring Values at Different Time Intervals
Mansur Shomali, MD, CM, Junjie Luo, MS, Abhimanyu Kumbara, MS, Anand Iyer, PhD, Gordon Gao, PhD
CGM-GPT: A Transformer Based Glucose Prediction Model to Predict Glucose Trajectories at Different Time Horizons
This research is the first in a series outlining Welldoc's novel methodology towards predicting future continuous glucose monitoring (CGM) glucose levels with high accuracy. The poster presents Welldoc's state-of-the-art AI models, which can predict glucose trajectories at 30-, 60- and 120-minute intervals for both type 1 and type 2 diabetes populations with ~ 50% less root mean square error, when compared to existing benchmark studies. Welldoc's GPT model, CGM-GPT, was trained only on CGM data sets from individuals living with Type 1 and Type 2 diabetes, and reflects the advanced opportunity to develop sophisticated large sensor models (LSM) by leveraging the vast data available via real-time sensors.
We are committed to further refining our models by incorporating additional data sources and exploring expanded applications. This ongoing research will drive the development of innovative diabetes management solutions. Stay tuned for additional information on our exciting transformer based CGM research.
Junjie Luo, MS, Abhimanyu Kumbara, MS, Mansur Shomali, MD, CM, Anand Iyer, PhD, Gordon Gao, PhD