[{"innerBlocks":[],"name":"core\/freeform","postId":611,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"The academic medical center of the University of Michigan is leveraging investments in artificial intelligence, machine learning and advanced analytics to unlock the value of its health data. According to Andrew Rosenberg, MD, chief information officer for Michigan Medicine, the organization currently has 34 ongoing AI and machine leaning projects, 28 of which have principal investigators. \u201cThere\u2019s a lot of collaboration around these projects\u2014as there should be for the diversity of thought and background needed to deal with complex problems\u2014working with at least seven other U of M schools,\u201d Rosenberg told the Machine Learning for Health Care conference on Friday in Ann Arbor, Mich. \u201cThat\u2019s one of the powers that we enjoy.\u201d

Also See<\/b>: U-Michigan to invest $100M in big data initiative<\/a> One of the machine learning projects cited by Rosenberg leverages a combination of electronic health records, monitor data and analytics to predict acute hemodynamic instability\u2014when blood flow drops and deprives the body of oxygen\u2014which is one of the most common causes of death for critically ill or injured patients.A team at the Michigan Center for Integrative Research in Critical Care developed an automated computer algorithm that utilizes data from a single lead of a non-invasive electrocardiograph signal for analysis and early identification of hemodynamic decline.Rosenberg contends that the technology\u2014called Analytic for Hemodynamic Instability\u2014is a novel application of continuous nonlinear pattern recognition through the analysis of heart rate variability, which can detect signs of hemodynamic decompensation prior to overt changes in vital signs over very short periods of time.Another project highlighted by Rosenberg targets clostridium difficile, an aggressive gut-infecting bacteria that is resistant to many common antibiotics. Michigan researchers teamed with investigators from Massachusetts General Hospital and the Massachusetts Institute of Technology to develop machine learning models that can predict a patient\u2019s risk of developing C. difficile much earlier than can be diagnosed with current methods.\u201cIt\u2019s an extremely resistant-to-cleaning type of infection that is in some ways very preventable, but when someone gets it, there\u2019s a really high morbidity and even mortality,\u201d said Rosenberg. The good news, he added, is that \u201cthere\u2019s enough features\u201d in Michigan\u2019s clinical data that are amenable to the modeling.In this big data approach, analysis of EHRs is able to predict a patient\u2019s C. difficile risk throughout the course of hospitalization. In addition, the models are specifically tailored to individual institutions to accommodate different EHR systms and patient populations.","saveContent":"The academic medical center of the University of Michigan is leveraging investments in artificial intelligence, machine learning and advanced analytics to unlock the value of its health data. According to Andrew Rosenberg, MD, chief information officer for Michigan Medicine, the organization currently has 34 ongoing AI and machine leaning projects, 28 of which have principal investigators. \u201cThere\u2019s a lot of collaboration around these projects\u2014as there should be for the diversity of thought and background needed to deal with complex problems\u2014working with at least seven other U of M schools,\u201d Rosenberg told the Machine Learning for Health Care conference on Friday in Ann Arbor, Mich. \u201cThat\u2019s one of the powers that we enjoy.\u201d Also See<\/b>: U-Michigan to invest $100M in big data initiative<\/a> One of the machine learning projects cited by Rosenberg leverages a combination of electronic health records, monitor data and analytics to predict acute hemodynamic instability\u2014when blood flow drops and deprives the body of oxygen\u2014which is one of the most common causes of death for critically ill or injured patients.A team at the Michigan Center for Integrative Research in Critical Care developed an automated computer algorithm that utilizes data from a single lead of a non-invasive electrocardiograph signal for analysis and early identification of hemodynamic decline.Rosenberg contends that the technology\u2014called Analytic for Hemodynamic Instability\u2014is a novel application of continuous nonlinear pattern recognition through the analysis of heart rate variability, which can detect signs of hemodynamic decompensation prior to overt changes in vital signs over very short periods of time.Another project highlighted by Rosenberg targets clostridium difficile, an aggressive gut-infecting bacteria that is resistant to many common antibiotics. Michigan researchers teamed with investigators from Massachusetts General Hospital and the Massachusetts Institute of Technology to develop machine learning models that can predict a patient\u2019s risk of developing C. difficile much earlier than can be diagnosed with current methods.\u201cIt\u2019s an extremely resistant-to-cleaning type of infection that is in some ways very preventable, but when someone gets it, there\u2019s a really high morbidity and even mortality,\u201d said Rosenberg. The good news, he added, is that \u201cthere\u2019s enough features\u201d in Michigan\u2019s clinical data that are amenable to the modeling.In this big data approach, analysis of EHRs is able to predict a patient\u2019s C. difficile risk throughout the course of hospitalization. In addition, the models are specifically tailored to individual institutions to accommodate different EHR systms and patient populations.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

Michigan Medicine makes AI, machine learning a top tech priority

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