Michael Grasso - Research


Research Interests

The focus of my research is on innovative applications of biomedical informatics that expand the scope of clinical medicine, have a strong theoretical basis in computer science, and are of strategic importance to the University of Maryland Medical System. I am developing new approaches to knowledge representation and reasoning, which are optimized for very large clinical repositories. My work is looking at new methods for disease prediction, surveillance, treatment, and prevention, and for new ways to create personalized therapies based on an individual’s clinical, genetic, environmental, and lifestyle characteristics. The clinical focus for this work includes several chronic diseases and mental health conditions, healthy aging and longevity, and patient safety and quality improvement.

Current Research Projects

  • Knowledge Representation and Reasoning with Big Data - I am developing new methods for knowledge representation and reasoning that are optimized for very large clinical repositories, and which can be correlated with genomic and environmental data. My specific approach is to enhance machine learning algorithms with semantic analysis, domain information, and deep learning. My clinical focus is on coronary artery disease, diabetes, rheumatoid and osteo arthritis, chronic kidney disease, chronic pain, addiction, and mental illness. This work will help to identify new approaches to quantify clinical risk and enable clinical decision support, which can be applied to disease prediction, critical event prediction, and treatment efficacy prediction. I am 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, and which I am augmenting with clinical data from several other sources.

  • Patient Safety and Quality Measures in Emergency Medicine - Patient safety and quality is one of the nation's most important health care challenges. The widely-cited report by the Institute of Medicine estimates that as many as 44,000 to 98,000 people die in U.S. hospitals each year as the result of lapses in patient safety. This is especially important in the emergency department, where health care teams are challenged to rapidly diagnose and treat multiple patients, some of whom present with potentially life-threatening illness. I am conducting research in resource utilitzation 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.

Graduate Students

  • Miguel Ossandon, 2009, PhD Student, visual sensory substitution
  • Xianshu Zhu, 2009, MS Student, clinical image processing
  • Dashana Dalvi, 2009-2010, MS Student, clinical image processing
  • David Chapman, 2010, PhD Student, clinical image processing with semantic metadata in cell biology
  • Justin martineua, 2010-2011, PhD Student, clinical image processing with semantic metadata in cell biology
  • Ronil Mokashi, 2009-2011, MS Student, clinical image processing with semantic metadata in cell biology
  • Ashwini Lahane, 2010-2011, MS Student, Thesis Committee, clinical image processing
  • Ronil Mokashi, 2011, MS Student, Thesis Committee, clinical image processing
  • Dashana Dalvi, 2011, MS Student, Thesis Committee, genomic prediction of type 2 diabetes
  • Aniket Bochare, 2011-2012, MS Student, Thesis Committee, domain knowledge and genomic prediction
  • Ronil Mokashi, 2011-2012, MS Student, Thesis Committee, genomic association and machine learning
  • Zachary Kurtz, 2012, MS Student, Thesis Committee, simultaneous feature and cost estimation
  • Eduardo Llamas, 2012, MS Student, Thesis Committee, extraction of drug-gene relationships
  • Soma Das, 2011-2012, MS Student, Thesis Advisor, genomic prediction of coronary artery disease
  • Matthew Gately, 2011-2012, MS Student, Thesis Advisor, personalized medicine
  • Isaac Mativo, 2012-2016, PhD Student, Thesis Advisor, predictive models for clinical decision support
  • Christopher Chen, 2013, SRTP Summer Medical Student, genomic prediction of diabetes using biological enrichment
  • Sandeep Regmi, 2014, Medical Resident, wearable computing for clinical decision support
  • Matthew Lotz, 2015, PRISM Summer Medical Student, genomic prediction of coronary artery disease using biological enrichment
  • Rose Yesha, 2016, PhD Student, Thesis Committee, automated methods for analyzing unstructured medical data

Past Research Projects

  • Syndromic surveillance for bioterorism and medical disasters.
  • A computerized approach to glycemic control.
  • Identification and analysis of TSH-assoicated SNPs.
  • Assocation between ventricular assist devices and gastrointestinal bleeding.
  • Efforts to minimize adverse events by cross-covering physicians.
  • Medical response to terrorist attacks and other disasters.
  • Clinical trials data collection using handheld computers.
  • Voice-driven clinical data collection.
  • Computational image classification.
  • Intelligent software agents for disaster management.

Fisher Exact Test  Odds Ratio  Logistic Regression  T Test  CI  Pearson  Other  ANOVA  Sample Size

NCBI  HapMap  Blast  David  Seattle SNP  GenMAPP  Vista  Ingenuity