IBM Watson Health showcases progress tackling diabetes at American Diabetes Association’s 76th scientific sessions

NEW ORLEANS - 12 Jun 2016: Industry leaders, developers and researchers at the American Diabetes Association’s 76th Scientific Sessions are demonstrating how they are tapping into Watson cognitive computing to transform the lives of people with diabetes. Today at the Scientific Sessions, IBM (NYSE: IBM) unveiled a broad-based collaboration with the American Diabetes Association starting with a challenge to the worldwide developer community to build cognitive apps that reimagine how diabetes may be prevented, identified and managed. IBM also announced progress on its projects with Medtronic, featured work from the Watson developer community and introduced a new research project to better understand diabetic retinopathy, a leading cause of blindness that afflicts one in three people with diabetes.

“At IBM, we know it will take more than one entity to transform healthcare, and that’s why Watson Health has assembled a large and diverse ecosystem to collaboratively rethink diabetes,” said Deborah DiSanzo, General Manager, IBM Watson Health. “This ecosystem features researchers studying the condition, developers innovating digital solutions with Watson APIs, and global leaders Medtronic, Novo Nordisk and -- now -- the American Diabetes Association, which sets the clinical standards for diabetes care worldwide. The addition of the Association to the ecosystem is a powerful validation of the promise of Watson.”

Medtronic & Watson Health Reveal New Data, Cognitive App Name: “SugarWise TM

Medtronic and IBM revealed that the first-of-its-kind Medtronic cognitive app has advanced to the final development stage. The app will be named SugarWiseTM , relating to a key diabetes concern (sugar) and the intelligence the organizations hope to deliver (wise). The Medtronic app is designed to help make daily diabetes management easier and potentially more effective.

The first generation of the Medtronic SugarWise app is expected to uncover important patterns and trends – based on retrospective analysis of patients’ insulin, continuous glucose monitors and nutritional data – to help people understand how their behavior affects their glucose level in real time. In addition, to further advance the capabilities of future generations of the SugarWise app, the companies have revealed new results on hypoglycemia prediction using data from 10,000 anonymized patients which demonstrated preliminary prediction accuracy of 75-86 percent within a 2-4 hour window.[1]

Israeli Startup HelpAround Optimizes Patient Support with Watson

Developers at digital health startup HelpAround are building Watson APIs into the company’s diabetes support community to optimize their ability to analyze patients’ questions and requests in real-time. The HelpAround mobile platform provides quality-of-life support to people living with diabetes by matching them with a resource that could help. Watson will enable HelpAround to analyze every help request in real-time, assess its sentiment and tone, and identify frustrations, dissatisfaction and expressions of urgency.

Insights from Watson are designed to enable the HelpAround platform to better personalize support offered to its users, almost instantaneously. For example, recognizing in real-time that an individual is in distress in regards to their insulin or glucose levels will allow HelpAround to connect the patient with other insulin users or with a nearby retailer, as well as the opportunity to chat with a representative of the company that manufactures the insulin in order to provide the patient with extra support.

“The unique cognitive capabilities of Watson will help us to significantly improve the quality of life of people living with diabetes, as well as their families,” said Yishai Knobel, founder and CEO of HelpAround.

IBM Research Studies Diabetic Retinopathy Risk Analysis, Prevention

Advances in diabetes data solutions from IBM Research have made IBM a leader in deriving cognitive insights from the growing volume of diabetes data. Over the past six years, IBM Research has published a number of studies that help to advance the understanding of diabetes through predictive modeling, care management, prevention and disease modeling, and in just the past year, IBM Research developed a first-of-a-kind predictive model to provide individualized ranking of diabetes risk factors for patients.[2] This body of work includes joint research with major academic institutions around the world.

In collaboration with Israeli Health Maintenance Organization Maccabi Healthcare services, IBM Research is using the latest advances in cognitive computing to build a predictive model that could help care providers design better personalized care management plans for the early detection of diabetic retinopathy, the leading cause of blindness for people with diabetes.

The study will use machine learning to find patterns in large amounts of high dimensional, retrospective clinical data, based on data from Maccabi’s completely anonymized diabetic patient database. This data collection, which includes 20 years of data on 2 million members – roughly 25 percent of the Israeli population – will be used to design a model that can predict the need for eye examination, as well as the optimal intervals between eye examinations, personalized to the needs of each individual.

“Technology promises to transform the way doctors and patients battle disease, especially complex chronic illnesses like diabetes,” said Prof. Varda Shalev, Director of the Institute for Health Research and Innovation at Maccabi Healthcare Services. “The goal of our work with IBM Research is to discover new ways to significantly improve patient treatment and our ability to help prevent retinal degeneration for people with diabetes.”

[1] Preliminary research results - based on dataset in controlled environment - Data on File with Medtronic plc

[2] Ng, K., Sun, J., Hu, J., & Wang, F. (2015). Personalized Predictive Modeling and Risk Factor Identification using Patient Similarity. AMIA Summits on Translational Science Proceedings, 2015, 132–136.

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