Philadelphia, PA – Independence Blue Cross, New York University, and NYU Langone Medical Center have established a collaboration to develop machine-learning algorithms to spot cases of undiagnosed diabetes and to predict pre-diabetes in patients to improve care and lower costs.
The work will be supported by a three-year grant from Independence Blue Cross (IBC), the Philadelphia region’s leading health insurer. The collaboration is part of NYU’s Initiative in Data Science and Statistics, which aims to harness today’s torrent of data in order to make advances in medicine, science, technology, business, and a range of other fields. IBC is a founding member of this university-wide effort.
“Independence Blue Cross is proud to be a leader in exploring the use of sophisticated computer analytics to help transform the delivery and quality of health care,” said Daniel J. Hilferty, IBC president and CEO. “Partnering with NYU, one of the nation’s leading experts in the field of data analytics, will provide us with invaluable information we can use to greatly improve the health of our members and lower health care costs. After all diabetes is the number one chronic illness in the Philadelphia region.”
More than 8 percent or 25 million people in the U.S. suffer from diabetes, according to the American Diabetes Association. But nearly one-third of those afflicted with diabetes are undiagnosed —unaware that they have this disease. Moreover, it has recently been estimated that an additional 25 percent, or 79 million people, have “pre-diabetes,” a condition in which blood glucose levels are higher than normal, but not yet high enough to be diagnosed as diabetes. The pre-diabetes stage is a critical one. If hyperglycemia goes untreated, damage to the blood vessels begins and the risk of cardiovascular disease increases.
“Without a doubt, improved ways to accelerate the diagnosis of diabetes in affected people will improve health, reduce the complications of the disease, and reduce health care costs,” said Ann Marie Schmidt, M.D., a professor of endocrinology and medicine at NYU Langone Medical Center and co-investigator.
“Diabetes is a debilitating disease causing tremendous strain on our health care system,” said Somesh Nigam, senior vice president, chief informatics officer of IBC. “With this analysis, we can slow the progression of the diabetes epidemic by identifying people at high risk for diabetes, working with them and their doctors to get the care they need, and ultimately keeping people well, reducing their complications from diabetes, and lowering costs.”
NYU’s David Sontag, an assistant professor in the Department of Computer Science at Courant Institute of Mathematical Sciences, will head the collaboration.
The project’s other co-investigators will be Yann LeCun, a professor of computer science and neural science at the Courant Institute of Mathematical Sciences, and Saul Blecker, M.D., an assistant professor of population health and general internal medicine at NYU Langone Medical Center.
Under this collaboration, the researchers will develop methodologies to predict which patients have undiagnosed diabetes and pre-diabetes by applying machine-learning algorithms to IBC’s medical and pharmacy claims data.
Machine learning is a branch of artificial intelligence that involves computers “learning” to extract knowledge from massive data sets and rendering informed analyses and judgments, often predicting outcomes. This collaboration will use machine learning to develop an algorithm, using patients’ historical claims data, to predict which patients are at the highest risk of diabetes. All patient data is securely maintained at all times.
Data Science — using automated methods to analyze massive amounts of data and extract knowledge from them — is a set of methods that is becoming core to many areas of business, science, and government. Last year, the White House announced a multi-agency initiative in “Big Data” to speed the pace of discovery in science and engineering, strengthen national security, and transform teaching and learning. A growing number of companies including those in the pharmaceutical, financial, insurance and web industries are driven by Data Science.
For more on NYU’s Initiative in Data Science and Statistics, go to www.nyu.edu/datascience.
Founded in 1831, NYU is one of the world’s foremost research universities and is a member of the selective Association of American Universities. The first Global Network University, NYU has degree-granting university campuses in New York and Abu Dhabi, and has announced a third in Shanghai; has a dozen other global academic sites, including London, Paris, Florence, Tel Aviv, Buenos Aires, and Accra; and sends more students to study abroad than any other U.S. college or university. Through its numerous schools and colleges, NYU conducts research and provides education in the arts and sciences, law, medicine, business, dentistry, education, nursing, the cinematic and performing arts, music and studio arts, public administration, social work, and continuing and professional studies, among other areas. For more information visit http://www.nyu.edu/
About NYU Langone Medical Center
NYU Langone Medical Center, a world-class, patient-centered, integrated, academic medical center, is one of the nation’s premier centers for excellence in clinical care, biomedical research and medical education. Located in the heart of Manhattan, NYU Langone is composed of four hospitals – Tisch Hospital, its flagship acute care facility; the Hospital for Joint Diseases, recognized as one of the nation’s leading hospitals dedicated to orthopaedics and rheumatology; Hassenfeld Pediatric Center, a comprehensive pediatric hospital supporting a full array of children’s health services; and Rusk Rehabilitation, inpatient and outpatient therapy services devoted entirely to rehabilitation medicine – plus NYU School of Medicine, which since 1841 has trained thousands of physicians and scientists who have helped to shape the course of medical history. The medical center’s tri-fold mission to serve, teach and discover is achieved 365 days a year through the seamless integration of a culture devoted to excellence in patient care, education and research. For more information, go to www.NYULMC.org