The Requirements for Ontologies in Medical Data Integration: A Case Study

Evidence-based medicine is critically dependent on three sources of information: a medical knowledge base, the patients medical record and knowledge of available resources, including where appropriate, clinical protocols. Patient data is often scattered in a variety of databases and may, in a distributed model, be held across several disparate repositories. Consequently addressing the needs of an evidence-based medicine community presents issues of biomedical data integration, clinical interpretation and knowledge management. This paper outlines how the Health-e-Child project has approached the challenge of requirements specification for (bio-) medical data integration, from the level of cellular data, through disease to that of patient and population. The approach is illuminated through the requirements elicitation and analysis of Juvenile Idiopathic Arthritis (JIA), one of three diseases being studied in the EC-funded Health-e-Child project.Comment: 6 pages, 1 figure. Presented at the 11th International Database Engineering & Applications Symposium (Ideas2007). Banff, Canada September 200

Semantic visualization of patient information

Clinical practice and research rely increasingly on analytic approaches to patient data. Visualization enables the comparative exploration of similar patients, a key requirement in certain clinical decision support systems. Patient data is complex and heterogeneous, may have different formats, reside in various structures and carry different semantics. This makes the comparison and analysis of clinical data a challenging task. Most medical applications visualize patient data without integrating additional semantic information to structure the analysis. Our objective is to map patient data onto relevant fragments of ontologies and inferred ontological structures as a basis for improved patient data visualization, comparison, and analysis. Two visualization scenarios that we have implemented using the patient data acquired in the Health-e-Child project will be presented and their clinical evaluation will be provided. © 2008 IEEE