Michael Grasso - Research

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 specific research interests include clinical decision support systems, clinical data mining, clinical image processing, emergency preparedness, and personalized medicine.

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 a student or post-doc 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 a University of Maryland student, 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, Thesis Advisor, personalized medicine

Current Research Projects

• Personalized Medicine and Big Data Applied to Clinial Decision Support.

Although intuitive, the addition of genetic information to increase the accuracy of disease prediction remains an unproven hypothesis. We are experimenting with supervised learning methods, which combine clinical and gentic information, in order to increase the accuracy of disease-predition algorithms. We recently developed new disease prediction algorithms using machine learning techniques that combine clinical and genomic data. We are leading a four-campus research team, which is applying these new algorithms to several large clinical and genomic repositories, including data from VINCI, GENEVA, and the Million Veteran Project. Specifically, we are looking at factors that predict coronary artery disease and type 2 diabetes in pre-symptomatic individuals, predict critical events associated with these conditions, and predict the efficacy of various treatment options. This type of research will lead to personalized therapies based on an individual’s genetic and clinical characteristics.

• 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.

• Image Classification with Semantic Metadata in Cell Biology

Computational image classification is growing in importance in various fields, including cell biology. We propose to develop a database that can store images along with computer-extracted image features, and correlate these features to metadata that captures biological or clinical characteristics. Our initial focus is on the actin and myosin cytoskeleton. We are developing an image classifier using a support vector machine to map image features to biological characteristics. We are using the Oracle Relational Database platform to leverage its ability to handle both multimedia and semantic data efficiently. We are using a W3C semantic web approach to develop the ontology for the biological characteristics.

• Video Summarization for Laparoscopic Surgery
  
  Laparoscopic surgery is a minimally invasive technique that is the method of choice for a number of surgical procedures. Patients who undergo laparoscopic surgery have smaller scars, reduced pain, and a quicker recovery. The laparoscopic approach, however, is more technical challenging and has more demanding training requirements. Our overall goal is to develop a software tool to assist with video-based assessment of surgical trainees. We are developing an image classifier using machine learning approach to segment surgical videos into their basic steps, perform time and motion analysis, and provide a set of tools for review and evaluation.

• Computer-Aided Diagnosis of Endoscopic Images

Wireless capsule endoscopy is used to directly visualize parts of the small intestine previously unreachable by colonoscopy or upper endoscopy. As the capsule travels through the digestive system, it collects in excess of 50,000 images, which must be reviewed by a gastroenterologist. We are developing machine learning and image classification techniques to detect common lesions found in capsule endoscopic studies.

• Intelligent Software Agents for Disaster Management

  An agent framework is being developed 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.
   

• Virtual Reality Simulation for Medical Education
  
  Virtual reality environments such as Second Life are known to influence behavior. Learners become immersed in their own education through three-dimensional realism, role-play, and community interaction. We are using this approach to educate people about specific medical issues and to virtually experience the benefits and consequences of their behaviors. The educational areas we are working on include diabetes and cancer prevention.
   
• Personal Health Records
  
  Personal Health Records (PHR) are health records that are initiated and maintained by individuals. Examples of web-based records include Google Health and Microsoft HealthVault. Issues related to PHRs include trust, privacy, security, portability, mobility, medical identify theft, fidelity and completeness of the medical record, and the ability to interface with hospital-based and other clinical systems.

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.