Prognostic data-driven clinical decision support - formulation and implications.

Existing Clinical Decision Support Systems (CDSSs) typically rely on rule-based algorithms and focus on tasks like guidelines adherence and drug prescribing and monitoring. However, the increasing dominance of Electronic Health Record technologies and personalized medicine suggest great potential for prognostic data-driven CDSS. A major goal for such systems would be to accurately predict the outcome of patients' candidate treatments by statistical analysis of the clinical data stored at a Health Care Organization. We formally define the concepts involved in the development of such a system, highlight an inherent difficulty arising from bias in treatment allocation, and propose a general strategy to address this difficulty. Experiments over hypertension clinical data demonstrate the validity of our approach.

Biomedical data integration - capturing similarities while preserving disparities.

One of the challenges of healthcare data processing, analysis and warehousing is the integration of data gathered from disparate and diverse data sources. Promoting the adoption of worldwide accepted information standards along with common terminologies and the use of technologies derived from semantic web representation, is a suitable path to achieve that. To that end, the HL7 V3 Reference Information Model (RIM) [1] has been used as the underlying information model coupled with the Web Ontology Language (OWL) [2] as the semantic data integration technology. In this paper we depict a biomedical data integration process and demonstrate how it was used for integrating various data sources, containing clinical, environmental and genomic data, within Hypergenes, a European Commission funded project exploring the Essential Hypertension [3] disease model.