Reasoning with temporal data has attracted the attention of many researchers from different backgrounds including artificial intelligence, database management, computational linguistics and biomedical informatics. More specifically, activity detection is a very important problem in a wide variety of application domains such as video surveillance, cyber security, fault detection, but also clinical research. Thus, in this paper we present a prototype architecture designed and developed for activity detection in the medical context. In more detail, we first acquire data in real time 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, corresponding to specific performances obtained by the medical doctors when using the simulator. Some preliminary experiments using real data show the approach efficiency and effectiveness. Eventually, we also received positive feedbacks by the medical personnel who used our prototype.

D'Auria, D., Persia, F. (2014). Discovering expected activities in medical context scientific databases. In DATA 2014 - Proceedings of 3rd International Conference on Data Management Technologies and Applications (pp.446-453). SciTePress [10.5220/0005146504460453].

Discovering expected activities in medical context scientific databases

D'Auria, D;
2014

Abstract

Reasoning with temporal data has attracted the attention of many researchers from different backgrounds including artificial intelligence, database management, computational linguistics and biomedical informatics. More specifically, activity detection is a very important problem in a wide variety of application domains such as video surveillance, cyber security, fault detection, but also clinical research. Thus, in this paper we present a prototype architecture designed and developed for activity detection in the medical context. In more detail, we first acquire data in real time 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, corresponding to specific performances obtained by the medical doctors when using the simulator. Some preliminary experiments using real data show the approach efficiency and effectiveness. Eventually, we also received positive feedbacks by the medical personnel who used our prototype.
paper
Activity Detection; Cricothyrotomy Simulator; Medical Simulator; Scientific Databases;
English
3rd International Conference on Data Management Technologies and Applications, DATA 2014 - August 29-31, 2014
2014
DATA 2014 - Proceedings of 3rd International Conference on Data Management Technologies and Applications
9789897580352
2014
446
453
none
D'Auria, D., Persia, F. (2014). Discovering expected activities in medical context scientific databases. In DATA 2014 - Proceedings of 3rd International Conference on Data Management Technologies and Applications (pp.446-453). SciTePress [10.5220/0005146504460453].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/474000
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