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Can big data predict heart disease before today’s doctors can? IBM thinks so.
The company, joined by Sutter Health and Geisinger Health Systems, has received a $2 million grant to use big data analytics to detect the signs of heart disease years earlier than we can today.
The research, which started back in 2009, will comb through patients’ electronic health records, using data like demographics, medical history, and medication to find common signals indicative of heart disease. If things go right, the insights from the analysis will eventually be integrated into primary care, which should make it easier for doctors to predict which patients are at highest risk for the disease.
The research couldn’t come at a better time. Heart disease is the leading cause of death and hospitalization in the U.S., affecting 5.7 million people today. Half of those diagnosed with it die within five years — largely because by the time doctors detect the disease, it’s already done irreversible organ damage.
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IBM’s Shahram Ebadollahi, who is helping to lead the project, sums up the research well:
By pairing IBM’s expertise in Big Data analytics with the domain knowledge and data of our healthcare partners this project will result in the development of new analytic algorithms for more accurate detection of the early onset of heart failure.
IBM also points out that its findings could one day be extended to other diseases, which potentially means we could see similar predictive analytics for Alzheimer’s and perhaps even certain types of cancer.
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