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The aim of this study is the establishment of a warehouse physiological data and biomedical signal in intensive care adult patients in acute situations from particular records from the Philips Intellivue MP70 monitor.
Cardiopulmonary failures are major public health concerns, due to the aging population. Each of these situations is burdened with a poor prognosis in the medium term and a source of prolonged hospitalizations, generating significant health costs. Early detection and prediction of organ failure could reduce health costs and risks for the patient, offering a reaction early and appropriate medical technology. The proposed approach aims to optimize the knowledge of a complex physiological domain and multi-system, while promoting the automatic transfer of knowledge. The approach proposed data-mining and development of algorithms for detecting and / or predicting a strong potential for disruption because it proposes to apply innovative automated analysis procedures to a fragile patient population, and then a transfer to the medical device industry.
From communicating tools of recording of the signals, the investigator envisage in a global way:
1. the constitution of a warehouse of physiological data of grown-up patients in acute situation (intensive care unit);
2. the development by data mining of a system of detection of organs failures or adverse events basing itself on the application of innovative algorithms, allowing the decision-making operational, from the fusion of arisen ill-assorted events;
3. the use of intelligent tools of auto-learning and elaboration of complex multimodal models for purposes of prediction of events;
Observational Model: Cohort, Time Perspective: Prospective
CHRU de Brest
University Hospital, Brest
Published on BioPortfolio: 2016-09-08T16:53:21-0400
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A disease or state in which death is possible or imminent.
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Levels of severity of illness within a diagnostic group which are established by various measurement criteria.
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