DocumentCode :
3227650
Title :
A federated data collection application for the prediction of adverse hypotensive events
Author :
Stell, Anthony ; Sinnott, Richard ; Jiang, Jipu
Author_Institution :
Nat. e-Sci. Centre, Univ. of Glasgow, Glasgow, UK
fYear :
2009
fDate :
4-7 Nov. 2009
Firstpage :
1
Lastpage :
4
Abstract :
The Avert-IT project (EU FP7) is an initiative to develop a system that can predict the onset of hypotensive events in patients over a feasible timescale (e.g. 15 mins) and allow clinicians to administer the appropriate treatment. To produce this system requires the additional development of a data collection platform, based at six leading clinical centres throughout Europe, with real-time integration to established patient monitoring systems and conversion of this data to accepted standards developed previously by the Brain-IT consortium (www.brain-it.org). This paper describes the motivation of Avert-IT, the clinical background, the development and implementation of the data collection platform, and the design considerations behind the predictor system (¿Hypo-Predict¿ engine).
Keywords :
information technology; medical information systems; ontologies (artificial intelligence); patient monitoring; patient treatment; pattern recognition; standardisation; Avert-IT project; Brain-IT consortium; EU FP7; Hypo-Predict engine; adverse hypotensive events; federated data collection application; medical administration; patient monitoring systems; patient treatment; Biomedical monitoring; Engines; Europe; Information technology; Medical treatment; Ontologies; Patient monitoring; Real time systems; Standardization; Standards development; clinical information systems; ontologies; pattern recognition; standardization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Technology and Applications in Biomedicine, 2009. ITAB 2009. 9th International Conference on
Conference_Location :
Larnaca
Print_ISBN :
978-1-4244-5379-5
Electronic_ISBN :
978-1-4244-5379-5
Type :
conf
DOI :
10.1109/ITAB.2009.5394375
Filename :
5394375
Link To Document :
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