Diabetes and heart disease are two of the most costly and prevalent chronic conditions impacting patients in the US, leading the healthcare industry to spend billions every year to treat and manage these disorders.<\/p>\n
As care delivery continues to evolve from reactive disease treatment to proactive, preventive care, more organizations are looking to advanced technologies like artificial intelligence and machine learning to assist with drawing actionable conclusions from their big data resources.<\/p>\n \u201cThere is a lot of information about every one of us, in EHRs, in smart phones, in smart watches, and in other tracking devices<\/a>,\u201d Iaonnis Paschalidis, Professor of Engineering and Director of the Center for Information and Systems Engineering at Boston University, told HealthITAnalytics.com<\/em>.<\/p>\n \u201cWe can now analyze what happens to patients in real time and characterize the status and health condition of each individual.\u201d<\/a><\/p>\n Machine learning and AI approaches will prove invaluable for predicting events such as hospitalizations and readmissions, both of which account for a great deal of healthcare spending, Paschalidis noted.<\/p>\n \u201cAn AHRQ study estimates that on an annual basis, the US spends about $30 billion on hospitalizations that could have been prevented,\u201d he said.<\/p>\n \u201cAnd the majority of those costs, about 50 percent, are due to chronic diseases<\/a>. About 30 percent of that $30 billion is due to heart disease, and another 20 percent is due to diabetes. These are hospitalizations that could have been prevented with better care, and that’s a significant amount that could have been spent in other areas of need.\u201d<\/p>\n To help overcome these challenges and reduce unnecessary spending, Paschalidis is collaborating<\/a> with Boston Medical Center (BMC).<\/p>\n The two organizations will use a three-year, $900,000 grant from the National Science Foundation to develop machine learning algorithms that will identify patients at higher risk of heart disease or diabetes. Researchers will use the algorithms to enable early interventions and personalized treatments for high-risk patients.<\/p>\n \u201cWith machine learning methods, we can process very large amounts of data and examine the many factors that are involved in predicting a future outcome,\u201d Paschalidis said.<\/p>\n \u201cWe\u2019re aiming to deliver personalized predictions and personalized recommendations to individuals, with the goal of improving outcomes and reducing costs.\u201d<\/p>\n![\"Iaonnis](\"\/eng\/files\/2024\/02\/ece.faculty.24.paschalidis.jpg\")