Biomedical Informatics research covers a broad spectrum of inquiry – from the analysis of genomic microarray datasets to the evaluation of hospital organizations during the adoption of new technology. This spectrum reflects the many facets of Biomedical Informatics, which can be defined as “the scientific field that deals with biomedical information, data, and knowledge – their storage, retrieval, and optimal use for problem-solving and decision-making.” (Shortliffe & Blois, 2001). Below we present our faculty’s major funded research areas. We welcome your ideas for collaboration and invite individuals interested in training with us to contact us for further information.
Areas of Research
Our Centers and Labs
Our long-term goal is to understand the impact of interaction design in health information technology on medical cognition, human-computer interaction/efficiency, and system development. In the shorter term, we are identifying design patterns that reduce usability problems in electronic health records. Our approach is to provide a simplifying technology platform. The strength of our approach is its decision to give the nonprogrammer clinician end-user far greater control to design, gather, mashup, visualize and share clinical information in multiple ways.
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 Center for Clinical Artificial Intelligence (CCAI) is in the Department of Biomedical Informatics at the University of Pittsburgh and is directed by Shyam Visweswaran, MD, PhD. CCAI focuses on developing, implementing, and evaluating high performance clinical decision support (CDS) tools that are powered by artificial intelligence (AI) including machine learning (ML). AI has the potential to support, enable and improve medical decision-making to make it faster, accurate, and economical. In particular, AI-enabled predictions, monitoring, alerting will power the next generation of CDS.