Predicting ARDS using the MIMIC II physiological database
| Autor: | Aline Taoum, Farah Mourad-Chehade, Ziad Fawal, Hassan Amoud |
|---|---|
| Přispěvatelé: | Université Libanaise, Laboratoire Modélisation et Sûreté des Systèmes (LM2S), Institut Charles Delaunay (ICD), Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS)-Université de Technologie de Troyes (UTT)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2016 |
| Předmět: |
ARDS
Lung Respiratory rate Database business.industry Hospitalized patients 0206 medical engineering 02 engineering and technology Acute respiratory distress medicine.disease computer.software_genre 020601 biomedical engineering 03 medical and health sciences 0302 clinical medicine medicine.anatomical_structure Heart rate Medicine [SDV.IB]Life Sciences [q-bio]/Bioengineering 030212 general & internal medicine business Cardiac disorders [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing computer |
| Zdroj: | 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET) 2016 IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), Nov 2016, Beirut, Lebanon. pp.47-51, ⟨10.1109/IMCET.2016.7777425⟩ |
| DOI: | 10.1109/imcet.2016.7777425 |
| Popis: | International audience; Acute Respiratory Distress Syndrome (ARDS) is a critical lung condition occurring in ill patients. Like many other cardiac disorders, ARDS can be assessed by physiological measurements. This study aims to predict ARDS in hospitalized patients using only physiological signals as heart rate and breathing rate. An approach based on hypothesis testing is developed to detect whether subjects' signals deviate from their initial states. The approach is applied on mechanically ventilated subjects in the MIMIC II database. As results, a sensitivity going up to 85% is achieved, with a prediction remaining possible before 24 hours of ARDS occurrence. |
| Databáze: | OpenAIRE |
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