RODS—Real-time Outbreak and Disease Surveillance LaboratoryThe RODS Laboratory is a biosurveillance research laboratory at the University of Pittsburgh, Department of Biomedical Informatics. We are the home of the National Retail Data Monitor (NRDM), Pennsylvania RODS and the Real-time and Outbreak Surveillance Software.RODS Web Site
Centers, Labs and Projects
The Vis Lab
The Vis Lab is focused on the application of artificial intelligence and machine learning to problems in the Learning Health System (LHS) that include: 1) development of a learning Electronic Medical Record (LEMR) system, 2) precision medicine and personalized modeling, 3) reuse of Electronic Medical Record (EMR) data for clinical, translational, and informatics research, 4) data mining and causal discovery from biomedical data, and 5) automated visual analytics. The Vis Lab Website
TIES Cancer Research Network (TCRN)
The Text Information Extraction System (TIES) Cancer Research Network is a federated network of clinical reports and biospecimen registry to support and facilitate basic science, clinical, and translational research in cancer. TIES uses a sophisticated concept based search to retrieve pathology and radiology reports containing concepts of interest. Plans include enabling virtual slides and tissue microarray creation. The current participants in the network are University of Pittsburgh, University of Pennsylvania, Augusta University, Roswell Park Cancer Institute, Thomas Jefferson University and Stony Brook University. This work is funded by grant U24 CA180921 from NCI, NIH.
The TRanslational Informatics Applied to Drug Safety (TRIADS) lab focuses on the design and evaluation of intelligent clinical decision support interventions, distributed and collaborative knowledge bases, and advanced pharmacovigilance signal detection management. Students in the lab navigate the challenging interactions that occur between intelligent systems development and clinical use cases. The lab has applied advanced computational methods to address difficult challenges such as detecting bleeding adverse drug events, automatically identifying drug safety signals, intelligent drug-drug interaction alerting, and identifying patients who have an addressable risk of falls. Research currently happening in the lab includes the application of literature-based discovery to address confounding in observational health data, developing novel approaches to pharmacovigilance signal detection that use semantic predications extracted from the literature and drug product labeling, and...