{"id":1476,"date":"2019-08-22T18:14:16","date_gmt":"2019-08-22T22:14:16","guid":{"rendered":"https:\/\/sites.bu.edu\/paschalidis\/?p=1476"},"modified":"2019-08-22T18:21:01","modified_gmt":"2019-08-22T22:21:01","slug":"researchers-win-900k-nsf-grant-to-predict-heart-disease-diabetes-using-machine-learning","status":"publish","type":"post","link":"https:\/\/sites.bu.edu\/paschalidis\/2019\/08\/22\/researchers-win-900k-nsf-grant-to-predict-heart-disease-diabetes-using-machine-learning\/","title":{"rendered":"Researchers Win $900k NSF Grant to Predict Heart Disease, Diabetes Using Machine Learning"},"content":{"rendered":"
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By\u00a0Maureen Stanton<\/a>, CISE<\/em><\/h1>\n<\/header>\n
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Researchers (from left) Paschalidis, Mishuris, and Cassandras win $900K NSF grant to predict heart disease and diabetes using machine learning.<\/figcaption><\/figure>\n

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Researchers from the<\/span>\u00a0Boston University College of Engineering<\/span>\u00a0<\/span>and Boston Medical Center (BMC) will use a three-year, $900,000 grant from the National Science Foundation to develop and pilot a health informatics system to predict patients at risk of heart disease or diabetes, and enable early intervention and personalized treatment.<\/span><\/p>\n

\u201cOur research vision is to deliver personalized healthcare, from prediction to diagnostics to population health management,\u201d said Professor Ioannis\u00a0<\/span>Paschalidis (ECE, BME, SE), director of the Center for Information & Systems Engineering (CISE)<\/span>.\u00a0 \u201cIn earlier work<\/a>, we demonstrated how machine learning could predict hospitalizations due to these two chronic diseases about a year in advance with an accuracy rate of as much as 82 percent, a significant improvement over existing risk models \u2014 such as the Framingham study for cardiovascular disease.\u00a0 Now, with our synergistic team of scientists and physicians, we are developing more robust predictive methods and capabilities for offering personalized recommendations that can guide physicians and patients as they make health-related decisions. This is a new frontier in medical informatics research and we expect it will impact medical practice.\u201d<\/p>\n

Diagnosing these chronic diseases requires complex sets of clinical and pathological data, which often are not comprehensive, consistent, nor up to date for treating physicians.\u00a0 The result is that patients at higher risk often don\u2019t get needed treatment, while those at lower risk do, leading to poor patient outcomes and unnecessary costs. The new, interdisciplinary research collaboration will be led by\u00a0<\/span>Paschalidis<\/span>, CISE member Professor\u00a0<\/span>Christos Cassandras<\/span>\u00a0<\/span>(ECE, SE) and BMC Associate Chief Medical Information Officer and BU School of Medicine Assistant Professor of Medicine\u00a0<\/span>Rebecca Mishuris<\/span>. They will develop a new generation of predictive methods based on supervised machine learning techniques that are interpretable, have higher predictive power, and can handle more data.<\/p>\n

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The researchers will develop an approach based on novel mathematical methods and the requisite algorithms where electronic health records and real-time health data \u2014 including wearable, implantable, and home-based, networked diagnostic devices \u2013 can be used to develop prediction analytics that anticipate future events such as hospitalizations, re-admissions, and transitioning to an acute stage of a disease. These predictions trigger personalized interventions, ranging from increased monitoring and doctor visits to optimized treatment policies adapted to each patient.<\/p>\n

The researchers will also focus on enabling personalized treatment\u00a0based on learning\u00a0and optimizing\u00a0treatment protocols for chronic diseases. \u201cProtocols of this type are typically empirical, using a one-size-fits-all approach,\u201d explained Cassandras, head of the\u00a0<\/span>Systems Engineering Division<\/span>. \u201cThey assess the stage of the disease and adapt medication based on the stage but not the patient. By incorporating specific personalized interventions with recommendations, clinicians can, therefore, intervene before a patient\u2019s condition reaches a critical phase.\u201d<\/p>\n

In addition to methodological and algorithmic development, the project will pilot the newly developed algorithms by integrating them into the electronic health record system at BMC and with 14 affiliated Community Health Centers.<\/p>\n

\u201cPersonalized, predictive healthcare is the future of medicine,\u201d said Dr.\u00a0<\/span>Mishuris<\/span>.\u00a0 \u201cOur research is geared toward providing clinicians with powerful, interpretable data to achieve that goal. \u00a0Physicians are drowning in data and administrative processes.\u00a0 Our research approach will help physicians manage the deluge of clinical and patient data to make decisions in a more systematic fashion.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"

By\u00a0Maureen Stanton, CISE Researchers (from left) Paschalidis, Mishuris, and Cassandras win $900K NSF grant to predict heart disease and diabetes using machine learning.   Researchers from the\u00a0Boston University College of Engineering\u00a0and Boston Medical Center (BMC) will use a three-year, $900,000 grant from the National Science Foundation to develop and pilot a health informatics system to […]<\/p>\n","protected":false},"author":10488,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[4,2],"tags":[],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/posts\/1476"}],"collection":[{"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/users\/10488"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/comments?post=1476"}],"version-history":[{"count":4,"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/posts\/1476\/revisions"}],"predecessor-version":[{"id":1481,"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/posts\/1476\/revisions\/1481"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/media?parent=1476"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/categories?post=1476"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/sites.bu.edu\/paschalidis\/wp-json\/wp\/v2\/tags?post=1476"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}