Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks
The process of weaning from mechanical ventilation is one of the challenges in intensive care. 149 patients under extubation process (T-tube test) were studied: 88 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and...
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| Veröffentlicht in: | 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2009-01, Vol.2009, p.4343-4346 |
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| container_title | 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
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| creator | Arizmendi, Carlos Romero, Enrique Alquezar, Rene Caminal, Pere Diaz, Ivan Benito, Salvador Giraldo, Beatriz F. |
| description | The process of weaning from mechanical ventilation is one of the challenges in intensive care. 149 patients under extubation process (T-tube test) were studied: 88 patients with successful trials (group S), 38 patients who failed to maintain spontaneous breathing and were reconnected (group F), and 23 patients with successful test but that had to be reintubated before 48 hours (group R). Each patient was characterized using 8 time series and 6 statistics extracted from respiratory and cardiac signals. A moving window statistical analysis was applied obtaining for each patient a sequence of patterns of 48 features. Applying a cluster analysis two groups with the majority dataset were obtained. Neural networks were applied to discriminate between patients from groups S, F and R. The best performance obtained was 84.0% of well classified patients using a linear perceptron trained with a feature selection procedure (that selected 19 of the 48 features) and taking as input the main cluster centroid. However, the classification baseline 69.8% could not be improved when using the original set of patterns instead of the centroids to classify the patients. |
| doi_str_mv | 10.1109/IEMBS.2009.5332742 |
| format | Article |
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Each patient was characterized using 8 time series and 6 statistics extracted from respiratory and cardiac signals. A moving window statistical analysis was applied obtaining for each patient a sequence of patterns of 48 features. Applying a cluster analysis two groups with the majority dataset were obtained. Neural networks were applied to discriminate between patients from groups S, F and R. The best performance obtained was 84.0% of well classified patients using a linear perceptron trained with a feature selection procedure (that selected 19 of the 48 features) and taking as input the main cluster centroid. 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| subjects | Aplicacions de la informàtica Aprenentatge automàtic Bioinformàtica Cluster Analysis Computer Simulation Computers Data analysis Data Interpretation, Statistical Data mining Data processing Electrocardiography - methods Equipment Design Hospitals Humans Informàtica Intel·ligència artificial Machine learning Medical informatics Medicina Medicine Mineria de dades Models, Statistical Monitoring, Physiologic - methods Neural networks Neural Networks (Computer) Pattern analysis Respiration Respiration, Artificial - instrumentation Respiration, Artificial - methods Signal analysis Signal processing Statistical analysis Testing Ventilation Ventilator Weaning - methods Àrees temàtiques de la UPC |
| title | Data mining of patients on weaning trials from mechanical ventilation using cluster analysis and neural networks |
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