Discovering Expected Activities in Medical Context Scientific Databases

Daniela D'Auria, Fabio Persia

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.

References

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Paper Citation


in Harvard Style

D'Auria D. and Persia F. (2014). Discovering Expected Activities in Medical Context Scientific Databases . In Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: KomIS, (DATA 2014) ISBN 978-989-758-035-2, pages 446-453. DOI: 10.5220/0005146504460453


in Bibtex Style

@conference{komis14,
author={Daniela D'Auria and Fabio Persia},
title={Discovering Expected Activities in Medical Context Scientific Databases},
booktitle={Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: KomIS, (DATA 2014)},
year={2014},
pages={446-453},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005146504460453},
isbn={978-989-758-035-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of 3rd International Conference on Data Management Technologies and Applications - Volume 1: KomIS, (DATA 2014)
TI - Discovering Expected Activities in Medical Context Scientific Databases
SN - 978-989-758-035-2
AU - D'Auria D.
AU - Persia F.
PY - 2014
SP - 446
EP - 453
DO - 10.5220/0005146504460453