Mobile App Warns Diabetics of Dangerous Sugar Levels
By HospiMedica International staff writers Posted on 14 Jan 2019 |
Image: Watson Health helps diabetics keep in a healthy blood glucose range (Photo courtesy of IBM).
A new predictive tool analyzes blood glucose data to determine the likelihood of a hypoglyemic episode within the next several hours.
The Medtronic (Dublin, Ireland) Sugar.IQ app is a personal diabetes assistant with cognitive abilities that uses IBM (Armonk, NY, USA) Watson analytics capabilities to find patterns in diabetes data and offer real-time, personalized diabetes insights by continually analyzing how blood glucose levels respond to food intake, insulin dosages, daily routines, and other factors. Together with the Guardian Connect continuous glucose monitor (CGM), the Sugar.IQ app can turn trend patterns into personalized diabetes care.
Sugar.IQ is powered by the IBM Watson IQcast feature, which uses artificial intelligence (AI) predictive modelling to improve prognostic capabilities over time. A study presented at the last American Diabetes Association Scientific Sessions, which was held in July 2018 in Orlando (FL, USA), showed that Sugar.IQ users are more likely to achieve an extra 36 minutes per day in a healthy glucose range of 70-180 mg/dL, experiencing 30 minutes less time per day in hyperglycemia, and six minutes less daily in hypoglycemia.
“Avoiding complications like hypoglycemia is a tremendous burden, but fortunately it is a problem that can help be alleviated by learning from data, which is where AI comes in,” said Lisa Latts, MD, deputy chief health officer at IBM Watson Health. “Using machine learning models and predictive algorithms, IQcast analyzes multiple signals coming from a user, such as glucose levels, insulin data, food logs, and prior hypoglycemic events to assess whether a user has a low, medium, or high chance of experiencing hypoglycemia within the upcoming four hours.”
The Medtronic (Dublin, Ireland) Sugar.IQ app is a personal diabetes assistant with cognitive abilities that uses IBM (Armonk, NY, USA) Watson analytics capabilities to find patterns in diabetes data and offer real-time, personalized diabetes insights by continually analyzing how blood glucose levels respond to food intake, insulin dosages, daily routines, and other factors. Together with the Guardian Connect continuous glucose monitor (CGM), the Sugar.IQ app can turn trend patterns into personalized diabetes care.
Sugar.IQ is powered by the IBM Watson IQcast feature, which uses artificial intelligence (AI) predictive modelling to improve prognostic capabilities over time. A study presented at the last American Diabetes Association Scientific Sessions, which was held in July 2018 in Orlando (FL, USA), showed that Sugar.IQ users are more likely to achieve an extra 36 minutes per day in a healthy glucose range of 70-180 mg/dL, experiencing 30 minutes less time per day in hyperglycemia, and six minutes less daily in hypoglycemia.
“Avoiding complications like hypoglycemia is a tremendous burden, but fortunately it is a problem that can help be alleviated by learning from data, which is where AI comes in,” said Lisa Latts, MD, deputy chief health officer at IBM Watson Health. “Using machine learning models and predictive algorithms, IQcast analyzes multiple signals coming from a user, such as glucose levels, insulin data, food logs, and prior hypoglycemic events to assess whether a user has a low, medium, or high chance of experiencing hypoglycemia within the upcoming four hours.”
Latest Health IT News
- Machine Learning Model Improves Mortality Risk Prediction for Cardiac Surgery Patients
- Strategic Collaboration to Develop and Integrate Generative AI into Healthcare
- AI-Enabled Operating Rooms Solution Helps Hospitals Maximize Utilization and Unlock Capacity
- AI Predicts Pancreatic Cancer Three Years before Diagnosis from Patients’ Medical Records
- First Fully Autonomous Generative AI Personalized Medical Authorizations System Reduces Care Delay
- Electronic Health Records May Be Key to Improving Patient Care, Study Finds
- AI Trained for Specific Vocal Biomarkers Could Accurately Predict Coronary Artery Disease
- First-Ever AI Test for Early Diagnosis of Alzheimer’s to Be Expanded to Diagnosis of Parkinson’s Disease
- New Self-Learning AI-Based Algorithm Reads Electrocardiograms to Spot Unseen Signs of Heart Failure
- Autonomous Robot Performs COVID-19 Nasal Swab Tests
- Statistical Tool Predicts COVID-19 Peaks Worldwide
- Wireless-Controlled Soft Neural Implant Stimulates Brain Cells
- Tiny Polymer Stent Could Treat Pediatric Urethral Strictures
- Human Torso Simulator Helps Design Brace Innovations
- 3D Bioprinting Rebuilds the Human Heart