The sharing of medical data between different healthcare organizations in Europe must comply with the legislation of the Member State where the data were originally collected. These legal requirements may differ from one state to another. Privacy requirements such as patient consent may be subject to conflicting conditions between different national frameworks as well as between different legal and ethical frameworks within a single Member State. These circumstances have made the compliance management process in European healthgrids very challenging. In this paper, we present an approach to tackle these issues by relying on several technologies in the semantic Web stack. Our work suggests a direct mapping from high-level legislation on privacy and data protection to operational-level privacy-aware controls. Additionally, we suggest an architecture for the enforcement of these controls on access control models adopted in healthgrid security infrastructures.

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Predicting potential cancer treatment side effects at time of prescription could decrease potential health risks and achieve better patient satisfaction. This paper presents a new approach, founded on evidence-based medical knowledge, using as much information and proof as possible to help a computer program to predict bladder cancer treatment side effects and support the oncologist’s decision. This will help in deciding treatment options for patients with bladder malignancies. Bladder cancer knowledge is complex and requires simplification before any attempt to represent it in a formal or computerized manner. In this work we rely on the capabilities of OWL ontologies to seamlessly capture and conceptualize the required knowledge about this type of cancer and the underlying patient treatment process. Our ontology allows case-based reasoning to effectively predict treatment side effects for a given set of contextual information related to a specific medical case. The ontology is enriched with proofs and evidence collected from online biomedical research databases using “web crawlers”. We have exclusively designed the crawler algorithm to search for the required knowledge based on a set of specified keywords. Results from the study presented 80.3% of real reported bladder cancer treatment side-effects prediction and were close to really occurring adverse events recorded within the collected test samples when applying the approach. Evidence-based medicine combined with semantic knowledge-based models is prominent in generating predictions related to possible health concerns. The integration of a diversity of knowledge and evidence into one single integrated knowledge-base could dramatically enhance the process of predicting treatment risks and side effects applied to bladder cancer oncotherapy.