Introduction
Health informatics is the bridging of computer science, information and the health care field. This interdisciplinary field can be applied to a range of medical fields such as nursing, biomedicine, medicine and subspecialties such as immunology (immunoinformatics). Informatics not only has roles to play in day-to-day areas of immunology such as data storage/retrieval, decision support, standards and electronic health care records but also in research and education such as data mining and simulation systems (Coiera, 2002). Informatics and more specifically, health informatics first started being used in in the late 1950s with the rise of computers (Ho, 2010). Technologies such as computers allowed practitioners and researches
Data Mining
Data mining is of critical importance in the field of immunoinformatics. Genome sequencing of humans and animals have led to the accumulation of huge quantities of data (De Groot, Ardito, McClaine, Moise, & Martin, 2009). The intersection of experimental immunology and computational approaches both require access to good data that has been collected and stored in a efficient and organized way with international access (Jalbout, 2008). As a definition, data mining is the analysis step after data collection and databaseing has occurred. This is important interdisciplinary field of computer science as it allows the recognition of patterns in huge amounts of data. This is achieved through the use of statistics, artificial intelligence and database management. This analysis of data is integral to both research in the immunology field and also practicing immunological physicians (De Groot, Ardito, McClaine, Moise, & Martin, 2009). This is due to the fact that every day more genetic mapping of proteins, cells and cell processing are being fed into data bases. This information can then be used to discover otherwise unconceivable processes of the immunological system. Once patterns and therefore patterns are discovered, the information can then be fed into simulation systems (discussed later) to discover new medications and treatments for immune diseases. These prediction tools are then able to be used to create