Medical & Imaging Informatics research encompasses several areas of interrelated foci. Building on a longstanding history of NIH and NSF funding, our work spans the spectrum from cutting-edge methodological (algorithm) development through to translational applications and evaluation. We draw on interdisciplinary efforts and team science to create new paradigms for biomedical informatics research and healthcare.
Medical imaging is a mainstay in the screening and diagnosis of human disease, and in treatment response assessment. Imaging informatics is concerned with the management and analysis of such data (e.g., CT, MR, digital pathology, etc.) including quantitative analysis (e.g., radiomics) and its usage in the broader context of healthcare.
NATURAL LANGUAGE PROCESSING (NLP)
Biomedical natural language processing (NLP) encompasses methods for the automatic retrieval, extraction, and summarization of information found in clinical notes, published literature, social media, and other (unstructured) free-text data sources.
BIOMEDICAL KNOWLEDGE REPRESENTATION
Coalescing data into a useful form that can be reasoned with requires new ways of organization supporting efficient querying/discovery with deeper semantics. Biomedical knowledge representation is concerned with the data models, ontologies, and formalizations that structure data into useful information and insights.
Machine learning (ML) and reinforcement learning algorithms are making it possible to uncover new insights from the EHR and related observational datasets. This area of research aims to develop and apply new techniques to characterize and detect disease and to optimize actions. Issues related to how to systematically capture these insights in models and properly evaluate their efficacy are studied.
Clinical decision support (CDS) aims to provide important, timely information to physicians and patients. Research in this area draws on techniques from contemporary computational methods (e.g., sequential decision making); human-computer interaction; and behavioral and implementation science, helping optimize outcomes and healthcare delivery through informed decision-making.
Increased access to health devices and information create new opportunities for gathering data (e.g., through mobile health platforms, mHealth) as well as communicating and educating individuals (e.g., through patient portals). Digital health focuses on these emergent areas and socio-technological themes.