Healthcare informatics focuses on the efficient and effective acquisition, management, and use of information in healthcare. Advancing health informatics has been declared a grand challenge by the National Academy of Engineering and is a major area of emphasis for agencies such as the Centers for Medicare and Medicaid Services. As such, it has been identified as an area of national need. Sample uses of AI in health informatics includes expert systems for decision support, machine learning and data mining to discover patterns across patients, image analysis to assist in diagnosis, and natural language processing to extract information from free text medical documents. The areas of interest for this track include healthcare decision support, medical image processing, machine learning and data mining in healthcare, processing and managing patient records, syndromic surveillance, drug discovery, and personalization of clinical care.

There are two primary types of graph-based data miners: frequent subgraph and compression-based miners. With frequent subgraph miners, the most interesting substructure is the largest one (or ones) that meet the minimum support. Whereas, compression-based graph miners discover those subgraphs that maximize the amount of compression that a particular substructure provides a graph. The algorithms associated with these two approaches are not only different, but they also may result in dramatic performance differences, as well as in the normative patterns being discovered. In order to compare these two types of graph-based approaches to knowledge discovery, in the following sections we will compare two publicly available applications: GASTON and SUBDUE.