The ObTiMA system - ontology-based managing of clinical trials.

Clinical Trial Management Systems promise to help researchers in managing the large amounts of data occurring in clinical trials. In such systems Case Report Forms for capturing all patient data can usually be defined freely for a given trial. But if database definitions are automatically derived from such trial-specific definitions then the collected data cannot be easily compared to or integrated into other trials. We address this interoperability issue with an approach based on ontology and semantic data mediation. This resulted in the development of the ObTiMA system which is composed of a component for setting-up clinical trials and another for handling patient data during trials. Both components offer data reusability by relying on shared concepts defined in an ontology covering the whole cancer care and research spectrum.

A computational platform for continuous seizure anticipation, monitoring and clinical evaluation.

The development of platforms that are able to continuously monitor and handle epileptic seizures in a non invasive manner is of great importance as they would improve the quality of life of drug resistant epileptic patients. In this work, a device and a computational platform is presented for acquiring low noise electroencephalographic signals, for the detection/prediction of epileptic seizures and the storage of ictal activity in an electronic personal health record. In order to develop this platform, a systematic clinical protocol was established including a number of drug resistant children from the University Hospital of Heraklion. Dry electrodes with innovative micro-spike design were proposed in order to increase the signal to noise ratio of the recorded EEG signals. A wearable low cost platform and its corresponding wireless communication protocol was developed focus on minimizing the interference with the patient's body. A computational subsystem with advanced algorithms provides detection/anticipation of upcoming seizure activity and aims to protect the patient from an accident due to a seizure or to improve his/her social life. Finally, the seizure activity information is stored in an electronic health record for further clinical evaluation.

Designing a patient monitoring system for bipolar disorder using Semantic Web technologies.

The new movement to personalize treatment plans and improve prediction capabilities is greatly facilitated by intelligent remote patient monitoring and risk prevention. This paper focuses on patients suffering from bipolar disorder, a mental illness characterized by severe mood swings. We exploit the advantages of Semantic Web and Electronic Health Record Technologies to develop a patient monitoring platform to support clinicians. Relying on intelligently filtering of clinical evidence-based information and individual-specific knowledge, we aim to provide recommendations for treatment and monitoring at appropriate time or concluding into alerts for serious shifts in mood and patients' non response to treatment.