The University of Pittsburgh Medical Center is pairing with two local universities to expand research in health data analytics, and will pay for much of the work done. In turn, UPMC hopes to commercialize new medical innovations developed through the collaboration.
Targets for analytics research include helping clinicians and public health authorities quickly detect new disease outbreaks, smartphone apps that suggest a personalized dietary change most beneficial to an individual based on genetics and medical history, and early detection of donated organ rejection, among others.
UPMC over six years will fund new research centers at University of Pittsburgh and Carnegie Mellon University under the Pittsburgh Health Data Alliance. The delivery system will spend $10 million to $20 million annually each of the six years, UPMC CEO Jeffrey Romoff said at a March 16 press conference. Hundreds of millions of dollars in existing grant funds at the institutions also will be used for the venture.
The alliance starts with two research and development centers. The Center for Machine Learning and Health at Carnegie Mellon will focus on big health data analytics, personalized medicine and disease modeling, privacy and security issues with big data, patient and provider education, and a new general framework for big data in the healthcare environment.
The Center for Commercial Applications of Healthcare Data at U-Pitt will analyze personalized medicine for treating certain cancers and lung diseases, genomic and imaging data, and methods for data capture and analysis to generate actionable information.
As big data expands, work is needed to better understand the roles of privacy and security, says Eric Xing, PhD, a professor and director of the Center for Machine Learning and Health at Carnegie Mellon. In big data, a patient is no longer just an example of a particular disease, but is a unique individual and will have unique genetic risks, lifestyles and social environments that must be incorporated into treatment.
So the challenge is to keep patients unique yet also connected to the rest of the world to get more accurate diagnoses and treatments. The “machine learning” component of Carnegie Mellon’s work is similar to some degree to development of the IBM Watson computer which learns as it is fed information, and later can retrieve relevant data. But the big data initiative in Pittsburgh targets health care with the goal of technology being able to assess the risk and nature of disease and provide physicians with more focused and relevant questions.
While Carnegie’s expertise is in artificial intelligence and big data infrastructure, organizing and enabling use of data via texts, images, video and other media, U-Pitt brings expertise in medical science content of biology and genomics, along with other disciplines.
The project is still in development and participants continue to come up with new ideas and agendas, so the project could go further, such as creating patient assistance mobile devices, and “smartifying” the entire ecosystem of healthcare, Xing says.
Article originally published by HealthData Management.
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