Kaouther Nouira

Intelligent Monitoring System for Intensive Care Units

We address in the present paper a medical monitoring system designed as a multi-agent based approach. Our system includes mainly numerous agents that act as correlated multi-agent sub-systems at the three layers of the whole monitoring infrastructure, to avoid non informative alarms and send effective alarms at time. The intelligence in the proposed monitoring system is provided by the use of time series technology. In fact, the capability of continuous learning of time series from the physiological variables allows the design of a system that monitors patients in real-time. Such system is a contrast to the classical threshold-based monitoring system actually present in the Intensive Care Units (ICUs) which causes a huge number of irrelevant alarms.

Time Series Analysis and Outlier Detection in Intensive Care Data

In intensive care units, clinical information systems record a huge number of variables in the purpose of using them in medical decision making. Those variables are controlled by alarm systems based on fixed thresholds. This kind of systems produce alerts each time that a sudden shift as outlier, level change point or trend occurs and exceeds the threshold. In practice, we can see that a big number of alarms are false; this is due to the presence of non-symptomatic outliers. In this paper we aim to present some methods that can be helpful to detect this kind of outliers. And later, they can be used in the development of intelligent alarm systems

Automatic approach to enrich databases using ontology: Application in medical domain

Abstract The enrichment of databases is fundamental to maintain them, as well as the consistency and accuracy of the data. The database becomes useless if it is not up to date. Since there are a large number of databases, an automatic enrichment approach is required. However, until now no efficient approach has been provided in order to cope with this problem. In this paper, we propose a new approach to automate the enrichment of databases. It is based on an ontology, which model domains through sets of concepts and semantic relationships established between them. The proposed approach presents a set of rules to analyze ontologies and databases components and filter subsequently the necessary ones for the database enrichment of databases. We applied our approach in the medical domain that is a renewable domain. Also, it is characterized by a large number of databases and ontologies, and a large volume of data. For experimentations, a platform is developed to test rules using medical databases and medical ontologies. As a result we obtain enriched databases with new components that are either tables, attributes, or records.

Ontology Based DataBase for computer medical system

Ontology Based DataBase (OBDB) is a database model that allows both ontologies, which is a formal representation of terms related to specific domain, and their instances to be stored and queried in a single database. In this paper, we propose the construction of OBDB for the development of medical system. The idea is to automatically construct OBDB using rules and used it in the construct of medical system. To demonstrate the relevance of the proposed approach, we compare with current approaches of OBDB.

RTSVM: Real time support vector machines

In this paper, we propose a new classification method that improves the support vector machines technique (SVM). It consists of the real time SVM (RTSVM) that uses an incremental version of SVM which is the LASVM. It also takes into account of new data over time. Actually, current classification techniques suffer from scalability problem. There is a permanent growing and evolution of data. Besides, there is a need of important memory capacity and execution time to deal with data stream. Although the improvement made to SVM to reduce the memory use and computational time in training phase, the obtained model in training phase cannot be applied to new observations in test phase without using the hole data. To overcome this issue and improve classification task in test phase, the RTSVM adapts the initial model produced by the LASVM. After that, the RTSVM updates and improves it in test phase by only using new data for re-training. As a result, our proposal considerably reduces the execution time and improves the accuracy especially in test phase. Empirical study shows RTSVM to be effective when using real-world datasets.