Federating distributed clinical data for the prediction of adverse hypotensive events
| Autor: | Jipu Jiang, Anthony Stell, Tim Howells, Barbara A. Gregson, Per Enblad, Juan Sahuquillo, Giuseppe Citerio, Ian Piper, I. Chambers, Pelle Nilsson, Arminas Ragauskas, Rob Donald, Karl L. Kiening, Richard O. Sinnott |
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| Přispěvatelé: | Stell, A, Sinnott, R, Jiang, J, Donald, R, Chambers, I, Citerio, G, Enblad, P, Gregson, B, Howells, T, Kiening, K, Nilsson, P, Ragauskas, A, Sahuquillo, J, Piper, I |
| Jazyk: | angličtina |
| Rok vydání: | 2009 |
| Předmět: |
QA75
medicine.medical_specialty Traumatic brain injury General Mathematics MEDLINE General Physics and Astronomy Blood Pressure ALARM Physics and Astronomy (all) Clinical grid Engineering (all) Heart Rate Health care Information system Mathematics (all) Medicine Humans Data federation Event (computing) business.industry Hypotension prediction General Engineering Bayes Theorem medicine.disease Health indicator R1 Blood pressure Brain Injuries Emergency medicine Clinical data Medical emergency Neural Networks Computer business hypotensive events |
| ISSN: | 1364-503X |
| Popis: | The ability to predict adverse hypotensive events, where a patient's arterial blood pressure drops to abnormally low (and dangerous) levels, would be of major benefit to the fields of primary and secondary health care, and especially to the traumatic brain injury domain. A wealth of data exist in health care systems providing information on the major health indicators of patients in hospitals (blood pressure, temperature, heart rate, etc.). It is believed that if enough of these data could be drawn together and analysed in a systematic way, then a system could be built that will trigger an alarm predicting the onset of a hypotensive event over a useful time scale, e.g. half an hour in advance. In such circumstances, avoidance measures can be taken to prevent such events arising. This is the basis for the Avert-IT project (http://www.avert-it.org), a collaborative EU-funded project involving the construction of a hypotension alarm system exploiting Bayesian neural networks using techniques of data federation to bring together the relevant information for study and system development. |
| Databáze: | OpenAIRE |
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