An application for finding expected activities in medical context scientific databases

Reasoning with temporal data has attracted the attention of many researchers from difierent backgrounds including artificial intelligence, database management, computational linguistics and biomedical informatics. Temporal information is crucial in the clinical domain espe- cially in clinical research. More specifically, activity detection is a very important problem in a wide variety of application domains such as video surveillance, cyber security and fault detection. In this paper, we present a prototype architecture designed and developed for activity detection in the medical context. In more detail, we first real-time acquire data from a cricothyrotomy simulator, when used by medical doctors, then we store the acquired data into a scientific database and finally we use an Activity Detection Engine for finding expected activities, correspond- ing to specific performances obtained by the medical doctors when using the simulator. Some preliminary experiments using real data show the approach efficiency and effectiveness.