Abstract
This paper describes the steps of building two and efficient monitoring systems in intensive care unit (ICU). We propose two new systems that deal with large data sets and solves the main problems of the current monitoring system. In fact, the current monitoring system in ICU has many issues to detect real states of patients namely critical and normal states. It frequently generates a high number of false alarms having bad effects on the working conditions. Besides, these alarms can threat the patient life by misleading medical staff. Our aim, in this paper, is to avoid false alarms and keep a high level of sensitivity by improving the current monitoring system. In addition, our purpose is to generate groups of patients suffering from similar diseases and building a general model for similar patients. The obtained models will make the classification of new patients possible. To this end, we combine two incremental versions of support vector machines mainly the LASVM and ISVM techniques with the k-prototypes clustering method. The first proposed system is the KP-ISVM which is based on the ISVM technique and the k-prototypes. The second one is called KP-LASVM which takes profits of both the LASVM by reducing the false alarms and the k-prototypes by selecting the appropriate model to start classifying new patients. Both of our proposals are characterized by dealing with large amount of data streams and adding new patients. However, the system using LASVM and k-prototypes i.e. the KP-LASVM has produced the best results compared to the others monitoring systems and based on different evaluation criteria. All experimental results using real-medical databases have been analyzed and have proved the performance of the KP-LASVM.
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Ben Rejab, F., Nouira, K., Trabelsi, A. (2014). Health Monitoring Systems Using Machine Learning Techniques. In: Chen, L., Kapoor, S., Bhatia, R. (eds) Intelligent Systems for Science and Information. Studies in Computational Intelligence, vol 542. Springer, Cham. https://doi.org/10.1007/978-3-319-04702-7_24
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DOI: https://doi.org/10.1007/978-3-319-04702-7_24
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