DocumentCode :
2112121
Title :
A data mining approach to reduce the false alarm rate of patient monitors
Author :
Baumgartner, Bernd ; Rodel, K. ; Knoll, Aaron
Author_Institution :
Dept. of Comput. Sci., Tech. Univ. Munchen, Garching, Germany
fYear :
2012
fDate :
Aug. 28 2012-Sept. 1 2012
Firstpage :
5935
Lastpage :
5938
Abstract :
Patient monitors in intensive care units trigger alarms if the state of the patient deteriorates or if there is a technical problem, e.g. loose sensors. Monitoring systems have a high sensitivity in order to detect relevant changes in the patient state. However, multiple studies revealed a high rate of either false or clinically not relevant alarms. It was found that the high rate of false alarms has a negative impact on both patients and staff. In this study we apply data mining methods to reduce the false alarm rate of monitoring systems. We follow a multi-parameter approach where multiple signals of a monitoring system are used to classify given alarm situations. In particular we focus on five alarm types and let our system decide whether the triggered alarm is clinically relevant or can be considered as a false alarm. Several classification algorithms (Naive Bayes, Decision Trees, SVM, kNN and Multi-Layer Perceptron) were evaluated. For training and test sets a subset of the freely available MIMIC II database was used. Alarm-specific classification accuracy was between 78.56% and 98.84%. Suppression rates for false alarms were between 75.24% and 99.23%. Classification results strongly depend on available training data, which is still limited in the intensive care domain. However, this study shows that data mining methods are useful and applicable for alarm classification.
Keywords :
Bayes methods; biomedical equipment; data mining; decision trees; medical signal processing; multilayer perceptrons; patient monitoring; support vector machines; MIMIC II database; SVM classifier; alarm specific classification accuracy; alarm triggering; classification algorithms; data mining approach; decision tree classifier; false alarm rate reduction; intensive care units; k-nearest neighbour classifier; kNN classifier; multilayer perceptron classifier; multiparameter approach; naive Bayes classifier; patient monitors; support vector machine classifier; Biomedical monitoring; Data mining; Decision trees; Electrocardiography; Feature extraction; Monitoring; Training; Equipment Failure; Humans; Information Storage and Retrieval; Monitoring, Physiologic;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2012 Annual International Conference of the IEEE
Conference_Location :
San Diego, CA
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4119-8
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/EMBC.2012.6347345
Filename :
6347345
Link To Document :
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