Biosketch
Michael Grasso is an Assistant Professor of Internal Medicine
and Emergency Medicine at the University of Maryland School of Medicine,
and an Adjunct Assistant Professor of
Computer Science
at the University of Maryland Baltimore County. He practices Emergency Medicine through the University of Maryland School of Medicine. He is also board certified in Clinical Informatics and is Director of the Clinical Informatics Group at the University of Maryland School of Medicine.
He earned a medical degree from the George Washington University and a PhD in Computer Science from the University of Maryland Baltimore County. He completed residency training at the University of Maryland School of Medicine. He is a member of the Upsilon Pi Epsilon Honor Society in the Computing Sciences, the Kane-King-Dodec Medical Honor Society, the William Beaumont Medical Research Honor Society, is a Fellow of the American College of Physicians
(FACP), and is a Fellow of the
American Medical Informatics Association (FAMIA).
He has been awarded more than $2,000,000 in grant and contract funding from the National Institutes of Health, the Food and Drug Administration, the National Institute of Standards and Technology, the National Aeronautics and Space Administration, and the Department of Defense. He has authored more than 70 refereed publications, and has more than 25 years of experience in Clinical Informatics and Scientific Computing with an emphasis on software engineering, clinical decision support, and clinical data mining. His research focuses on big data analytics applied to clinical data. He is currently working with the national clinical repository from the Veterans Health Administration, which contains data on more than 35 million patients from roughly 150 medical centers and 800 outpatient clinics.
He also works with the EPIC clinical repository from the 14 member
hospitals within the University of Maryland Medical System and the
Maryland Emergency Medicine Network. He is developing new methods for knowledge representation and reasoning that are optimized for very large clinical repositories, and which can be applied to disease prediction, critical event prediction, and treatment efficacy prediction.
The clinical focus for his work includes several chronic diseases, opioid misuse and addiction,
SARS-CoV-2 and COVID-19, and online consumer health information. He is also conducting research in resource utilization and recidivism in emergency medicine, with a focus on co-morbidities, key risk factors, adverse drug events, chronic pain, suicidality, addiction, utilization patterns, and clinical workflow.
Shorter Biosketch
Michael Grasso is an Assistant Professor of Internal Medicine
and Emergency Medicine at the University of Maryland School of
Medicine, and an Adjunct Assistant Professor of Computer Science
at the University of Maryland Baltimore County. He earned his
medical degree from George Washington University and a PhD in
Computer Science from the University of Maryland Baltimore
County. He completed residency and fellowship training at the
University of Maryland. He works clinically in the Department of
Emergency Medicine at the Baltimore VA Medical Center and the
University of Maryland Medical Center. He is Director of the
University of Maryland Clinical Informatics Group, is board
certified in Clinical Informatics, is a Fellow of the American
College of Physicians, and is a Fellow of the American Medical
Informatics Association. He has authored more than 70 refereed
publications, and has received grant funding from NIH, NIST,
DoD, NASA, and other sources. He has over 25 years of experience
in Clinical Informatics with an emphasis on clinical decision
support, data mining, and software engineering. He works with
the national clinical repository from the Veterans Health
Administration and the EPIC clinical repository from the 14
member hospitals within the University of Maryland Medical
System. He is developing new methods for knowledge
representation and reasoning that are optimized for very large
clinical repositories, and which can be applied to disease
prediction, critical event prediction, and treatment efficacy
prediction. The clinical focus for his work includes several
chronic diseases, opioid misuse and addiction, SARS-CoV-2 and
COVID-19, online consumer
health information, and quality improvement in Emergency
Medicine.
Very Short Biosketch
Michael Grasso is a physician and an Assistant Professor of
Internal Medicine and Emergency Medicine at the University
of Maryland School of Medicine. He also holds a PhD Computer
Science, is board-certified in Clinical Informatics, and
is Director of the University of Maryland Clinical
Informatics Group. He has authored more than 70 refereed
publications, and has received grant funding from NIH, NIST,
DoD, and other sources. His research focuses on knowledge
representation and reasoning, quality improvement in
Emergency Medicine, and online consumer health information.
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