University of Maryland Clinical Informatics Group
Department of Emergency Medicine
The Clinical Informatics Group focuses 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. We are developing new approaches to knowledge representation and reasoning, which are optimized for very large clinical repositories. This work will lead to new methods for disease prediction, surveillance, treatment, and prevention, which can be personalized to an individual’s clinical, genetic, environmental, and lifestyle characteristics. The clinical focus for this work includes several chronic diseases and mental health conditions, as well as patient safety and quality improvement in emergency medicine.
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. Our specific approach is to enhance machine learning algorithms with semantic analysis, domain information, and deep learning. Our clinical focus is on coronary artery disease, diabetes, rheumatoid arthritis, chronic kidney disease, chronic pain, addiction, and mental illness. 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 prediction. We are 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 we are augmenting with clinical and genomic 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. We are 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.
Grasso MA, Dezman ZD, Grasso CT, Jerrard DA. Opioid pain medication prescriptions obtained through emergency medical visits in the Veterans Health Administration. Journal of Opioid Management. 2017;13(2):77-84.
Grasso MA, Dezman ZD, Comer AC, Jerrard DA. The decline in hydrocodone/acetaminophen prescriptions in emergency departments in the Veterans Health Administration between 2009 to 2015. West J Emerg Med. 2016;17(4):396-403. Available online at http://escholarship.org/uc/item/29d2w30f/.
Grasso MA, Dezman ZD, Comer AC, Jerrard DA. Hydrocodone/acetaminophen prescribing in emergency departments in the Veterans Health Administration. American College of Emergency Medicine, Research Forum, 2016 Oct 15-18; Las Vegas, NV.
Magidson PD, Grasso MA, Dezman ZD, Comer AC, Hirshon JM, Opioid and non-opioid treatments association with repeat ED visits for geriatric VA patients presenting with back pain. American College of Emergency Medicine, Research Forum, 2016 Oct 15-18; Las Vegas, NV.
Grasso MA, Yesha Y, Rishe N, Kraus VB, Niskar A. A Big Data Approach for Selection of a Large Osteoarthritis Cohort. OARSI World Conference on Osteoarthrits. 2016 Mar 31-Apr 3.
Feng Y, Janeja VP, Yesha Y, Rishe N, Grasso MA, Niskar A. Classifying Primary Outcomes in Rheumatoid Arthritis: Knowledge Discovery from Clinical Trial Metadata. 5th IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 2015 Oct 15-17.
Grasso MA, DiRenzo DD, Yesha Y, Rishe ND, Niskar A. Validation of a Large Rheumatoid Arthritis Cohort and Preventive Health Screening. ACR/ARHP Annual Meeting; 2015 Nov 6-11; San Francicso, CA.
Grasso MA, Comer AC, DiRenzo DD, Yesha Y, Rishe ND. Using big data to evaluate the association between periodontal disease and rheumatoid arthritis. AMIA Annu Symp Proc. 2015 Nov 14-18; San Francisco, CA.
Payne E, Carlisle A, Desai S, Grasso MA. Using "Big Data" analytics and health care informatics to advance personalized health. APHA 143rd Annual Meeting and Expo. 2015 Oct 31-Nov 4; Chicago, IL.
Grasso MA, Cotter B, Jerrard DA. The impact of a follow-up clinic on unscheduled return emergency department visits. American College of Emergency Medicine, Research Forum. 2015 Oct 26-27; Boston, MA.
Kench A, Janeja VP, Yesha Y, Rishe N, Grasso MA, Niskar A. Clinico-genomic data analytics for precision diagnosis and disease management. IEEE International Conference on Healthcare Informatics (ICHI 2015) 2015 Oct 21-23; Dallax, Tx.
Yesha Y, Niskar A, Grasso MA, DuVall S, Rishe N. Big Data Analytics - Veterans Affairs Informatics and Computing Infrastructure (VINCI). The 4th International Congress on Personalized Medicine (UPCP 2015); 2015 Jun 18-19; Tel Aviv, Israel.
Rishe N, Niksar A, Yesha Y, Grasso MA, Lucic T. Methods for Moving Towards Pre-Clinical Diagnosis for Rheumatoid Arthritis. The 4th International Congress on Personalized Medicine (UPCP 2015); 2015 Jun 18-19; Tel Aviv, Israel.
Grasso MA, Lemkin DL, Bond MC. Subspecialty training in clinical informatics: Prerequisite activities for potential applicants. EM Resident. 2015.
Chen C, Grasso MA. Improving prediction of type 2 diabetes using genomic domain information. AMIA Annu Symp Proc. 2014.
Grasso CT. Joshi A. Longitudinal tracking of pain phenotypes in electronic health records using an SVM. AMIA Annu Symp Proc. 2014.
Bochare A, Gangopadhyay A, Yesha Ye, Joshi A, Yesha Ya, Grasso MA, Brady M, Rishe N. Integrating domain knowledge in supervised machine learning to assess the risk of breast cancer. International J of Medical Engineering and Informatics, 2014;6(2):87-99.
Korolev V, Joshi A, Yesha Y, Grasso MA. On Use of Machine Learning Techniques and Genotypes for Prediction of Chronic Diseases. AMIA Annu Symp Proc. 2013.
Dhariwal D, Joshi A, Grasso MA. Text and ontology driven clinical decision support system. AMIA Annu Symp Proc. 2013.
Bochare A, Gangopadhyay, Yesha Ye, Joshi A, Yesha Ya, Grasso MA, Brady M, Rishe N. Integrating domain knowledge in supervised machine learning to assess the risk of breast cancer. Third International Conference on Global Trends in Biomedical Informatics Research, Education and Globalization, 2012.
Kugaonkar R, Gangopadhyay A, Yesha Ye, Joshi A, Yesha Ya, Grasso MA, Brady M, Rishe N. Finding associations among SNPs for prostate cancer using collaborative filtering. ACM Sixth International Workshop on Data and Text Mining in Biomedical Informatics (DTMBIO). 2012 Oct;: 57-60.
Lahane A, Yesha Y, Grasso MA, Joshi A, Park A, Lo J. Detection of unsafe actions in laparoscopic cholecystectomy videos. ACM SIGHIT International Health Informatics Symposium (ACM IHI), 2012 Jan;:315-322.
Martineau J, Mokashi R, Chapman D, Grasso MA, Brady M, Yesha Y, Yesha Y, Cardone C, Dima A. Sub-cellular feature detection and automated extraction of collocated actin/myosin regions. ACM SIGHIT International Health Informatics Symposium (ACM IHI), 2012 Jan;:399-407.
Grasso MA, Dalvi D, Das S, Gately M, Korolev V, Yesha Y. Genetic information for chronic disease prediction. IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM). 2011 Nov;:997.
Grasso MA. Multi-Channel Image Browser for Feature Analysis of Smooth Muscle Cells. AMIA Annu Symp Proc, 2011.
Lahane A, Joshi A, Finin T, Yesha Y, Grasso M. Situation-aware system for a smart operating room. IBM CASCON, 2010.
Grasso MA, Mokashi R, Dalvi D, Dima AA, Cardone A, Bhadriraju K, Plant AL, Brady M, Yesha Y, Yesha Y. Image classification of vascular smooth muscle cells. ACM IHI, 2010.
Grasso MA, Mokashi R, Dima AA, Cardone A, Bhadriraju K, Plant AL, Brady M, Yesha Y. Image classification in cell biology. AMIA Annu Symp Proc, 2010.
Grasso MA, Dalvi D, Dima AA, Cardone A, Bhadriraju K, Plant AL, Brady M, Yesha Y. An elliptical fit algorithm to classify the actin cytoskeleton. AMIA Annu Symp Proc, 2010.
Grasso CT, Finin T, Grasso MA, Joshi A, Yesha Y. A framework for the use of autonomous intelligent agents in disaster management and response. AMIA Annu Symp Proc, 2010.
Cardone A, Bhadriraju K, Grasso MA, Gilsinn DE, Molek M, Mokashi R, Chalfoun J, Dima AA, Brady M, Plant AL, Yesha Y, Yesha Y. Multi-channel subcellular feature analysis and correlation. Bioimage Informatics Conference September 17-19, 2010.
Grasso MA, Finin TW, Zhu X, Joshi A, Yesha Y. Video summarization of laparoscopic cholecystectomies. AMIA Annu Symp Proc, 2009, Nov 14-18.
Grasso CT, Finin TW, Grasso MA, Yesha Y, Joshi A. Intelligent Agents and UICDS. U.S. Department of Homeland Security Workshop on Emergency Management (EMWS09), Nov 5-6, 2009.
University of Maryland Clinical Informatics Group
Department of Emergency Medicine
110 South Paca Street, 6th Floor, Suite 200, Baltimore, MD 21201