Dmitry Rogulin

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.

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.

Medical Data Integration and the Semantic Annotation of Medical Protocols

The integration of heterogeneous biomedical information is one important step towards providing the level of personalization required in the next generation of healthcare provision. In order to provide the computer-based decision support systems needed to access this integrated healthcare information it will be necessary to handle the semantics of (amongst other things) medical protocols. The EC FP6 Health-e-Child project aims to develop an integrated healthcare platform for European paediatrics and decision support tools to access personalized health information. This paper introduces both the integrated data model in the Health-e-Child project and through a case study using the brain tumour protocols it demonstrates the semantic annotation of patient data acquired in the project using UMLS as the primary source of semantic data.

An Architecture for Semantic Navigation and Reasoning with Patient Data - Experiences of the Health-e-Child Project

Medical ontologies have become the standard means of recording and accessing conceptualized biological and medical knowledge. The expressivity of these ontologies goes from simple concept lists through taxonomies to formal logical theories. In the context of patient information, their application is primarily annotation of medical (instance) data. To exploit higher expressivity, we propose an architecture which allows for reasoning on patient data using OWL DL ontologies. The implementation is carried out as part of the Health-e-Child platform prototype. We discuss the use case where ontologies establish a hierarchical classification of patients which in turn is used to aid the visualization of patient data. We briefly discuss the treemap-based patient viewer which has been evaluated in the Health-e-Child project.

Health-e-Child: A Grid Platform for European Paediatrics

The Health-e-Child (HeC) project [1], [2] is an EC Framework Programme 6 Integrated Project that aims to develop a grid-based integrated healthcare platform for paediatrics. Using this platform biomedical informaticians will integrate heterogeneous data and perform epidemiological studies across Europe. The resulting Grid enabled biomedical information platform will be supported by robust search, optimization and matching techniques for information collected in hospitals across Europe. In particular, paediatricians will be provided with decision support, knowledge discovery and disease modelling applications that will access data in hospitals in the UK, Italy and France, integrated via the Grid. For economy of scale, reusability, extensibility, and maintainability, HeC is being developed on top of an EGEE/gLite [3] based infrastructure that provides all the common data and computation management services required by the applications. This paper discusses some of the major challenges in bio-medical data integration and indicates how these will be resolved in the HeC system. HeC is presented as an example of how computer science (and, in particular Grid infrastructures) originating from high energy physics can be adapted for use by biomedical informaticians to deliver tangible real-world benefits.