Michael Grasso - Research

Research Interests

The focus of my research has been to develop new technologies that expand the scope of medicine and increase the diagnostic capabilities of physicians at the point of care. My research interests emphasize on software engineering, database theory, and human factors applied to clinical decision support systems, clinical data mining, clinical image processing, emergency preparedness, and patient safety.

Students and Post-Docs Who Want to Work in My Research Group

Great! I would love to see how we can work together. Please contact me if you are already a student, resident, or facutly at the University of Maryland School of Medicine, UMB, UMBC, College Park, or elsewhere in the University of Maryland system. However, if you are not currently a student or post-bacc, and are looking for an appointment at the University of Maryland, I unfortunately can't help you, and have no information about paid or volunteer positions. Instead, please follow the admissions process at the University of Maryland.

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, PhD Student, Thesis Advisor, personalized medicine
  • Sandeep Regmi, 2014, Medical Resident, wearable computing for clinical decision support
  • Adam Raby, 2014, MS Student, Thesis Advisor, wearable computing for clinical decision support

Current Research Projects

  • Knowledge Representation and Reasoning with Big Data - We are 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. This work will lead to new approaches to quantify clinical risk and enable clinical decision support. It can be applied to disease prediction, critical event prediction, and treatment efficacy. We are currently working with a large clinical repository with data on more than 20 million patients from roughly 150 medical centers and 800 outpatient clinics that contains over one billion patient encounters with associated vital sign, laboratory, radiology, health screening, and pharmacy data.

  • Patient Safety and Quality Measures in Emergency Medicine - Patient safety 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. Safety events can be difficult to measure, in part, because identification often depends on self-reporting, and also because we tend to discover only those events that result in significant harm to patients. We are therefore developing new computational methods to identify patient safety events that are based on semantic analysis and situational awareness, and which can be used by health care teams for computationally-enabled clinical decision support. We are also conducting research in the nature of clinical expertise in decision-making by individuals and by health care teams, to assist with the development of new approaches for computational systems.

  • Wearable and Mobile Computing for Clinical Decision Support at the Bedside - We hypothesize that a wearable computer, with essential clinical data and timely alerts, can help clinicians make better decisions at the point of care.We are working with the Google Glass wearable computing platform to develop a number of hands-free decision support tools, which can be used by physicians at the bedside.

  • Syndromic Surveillance - Syndromic surveillance is a process used for the early detection and monitoring of disease outbreaks attributable to biologic terrorism or natural causes. We are developing a new approach to syndromic surveillance by applying machine learning algorithms to both clinical data and social media.

Past Research Projects

  • A Computerized Approach to Glycemic Control

Lessons learned from prior efforts were used to design an optimal approach to computerize insulin protocols for critical care and general medicine patients that better fits into the existing physician workflow.

  • Identification and Analysis of a TSH-Associated SNP

An analysis of a TSH-associated SNP was performed to help identify which gene/variant was the most likely functional unit.

  • Ventricular Assist Devices and Gastrointestinal Bleeding

A retrospective analysis of consecutive VAD recipients found that age was the only independent predictor of gastrointestinal bleeding, and that nonpulsatile VADs were not associated with an increase in gastrointestinal bleeding versus there pulsatile counterparts.

  • Efforts to Minimize Adverse Events by Cross-Covering Physicians

An analysis of process improvement efforts regarding on the transfer or care was performed, and a software framework was developed to increase the accuracy and safety of this process.

  • Medical Response to Terrorist Attacks and Other Disasters

A redundant communication framework was developed to mobilize medical personnel and transfer information during a medical disaster.

  • Clinical Trials Data Collection Using Palm Computers

A handheld data collection system was developed to update and validate clinical information obtained at the bedside or in the field.

  • Voice-Driven Pathology Data Management System

A voice-driven system was developed to collect clinical data in hands-busy and eyes-busy environments.

  • Computational Image Classification

We developed image classification techniques to extract and map image features to biological characteristics. We applied this algorithm to laparoscopic surgery videos, in order to segment the videos into clinical steps and to recognize potentially unsafe actions. We also used this algorithm to extract geometric image feature from smooth muscle images to help characterize cell-to-matrix interactions.

  • Intelligent Software Agents for Disaster Management

We developed an agent framework for the Department of Homeland Security to support a peer-to-peer network of disaster responders using the Unified Incident Command and Decision Support (UICDS) platform. This is a middleware foundation that enables commercial and government incident management technologies to share information and support decisions for the National Response Framework and National Incident Management to prevent, protect, respond, and recover from natural, technological, and terrorist events.

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

NCBI  HapMap  Blast  David  Seattle SNP  GenMAPP  Vista  Ingenuity