Thinking PubMed: an innovative system for mental health domain

Information regarding mental illness is dispersed over various resources but even within a specific resource, such as PubMed, it is difficult to link this information, to share it and find specific information when needed. Specific and targeted searches are very difficult with current search engines as they look for the specific string of letters within the text rather than its meaning.In this paper we present Thinking PubMed as a system that results from synergy of ontology and data mining technologies and performs intelligent information searches using the domain ontology. Furthermore, the Thinking PubMed analyzes and links the retrieved information, and extracts hidden patterns and knowledge using data mining algorithms. This is a new generation of information-seeking tool where the ontology and data-mining work in concert to increase the value of the available information

Mining of health information from ontologies

Data mining techniques can be used to efficiently analyze semi-structured data. Semi-structured data are predominantly used within the health domain as they enable meaningful representations of the health information. Tree mining algorithms can efficiently extract frequent substructures from semi-structured knowledge representations. In this paper, we demonstrate application of the tree mining algorithms on the health information. We illustrate this on an example of Human Disease Ontology (HDO) which represents information about diseases in 4 ?dimensions?: (1) disease types, (2) phenotype (observable characteristics of an organism) or symptoms (3) causes related to the disease, namely genetic causes, environmental causes or micro-organisms, and (4) treatments available for the disease. The extracted data patterns can provide useful information to help in disease prevention, and assist in delivery of effective and efficient health service