Data mining: concepts and techniques
Data mining: concepts and techniques
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
Sensor Networks for Emergency Response: Challenges and Opportunities
IEEE Pervasive Computing
SAPhyRA: Stream Analysis for Physiological Risk Assessment
CBMS '07 Proceedings of the Twentieth IEEE International Symposium on Computer-Based Medical Systems
A new approach to the abstraction of monitoring data in intensive care
AIME'05 Proceedings of the 10th conference on Artificial Intelligence in Medicine
An open architecture patient monitoring system using standard technologies
IEEE Transactions on Information Technology in Biomedicine
A wireless PDA-based physiological monitoring system for patient transport
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
A Mobile Care System With Alert Mechanism
IEEE Transactions on Information Technology in Biomedicine
Incremental Diagnosis Method for Intelligent Wearable Sensor Systems
IEEE Transactions on Information Technology in Biomedicine
IEEE Transactions on Information Technology in Biomedicine
Introduction to the special section on computationalintelligence in medical systems
IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Triggers and Monitoring in Intelligent Personal Health Record
Journal of Medical Systems
Real-time analysis for short-term prognosis in intensive care
IBM Journal of Research and Development
Review: Knowledge discovery in medicine: Current issue and future trend
Expert Systems with Applications: An International Journal
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This paper presents a flexible framework that performs real-time analysis of physiological data to monitor people's health conditions in any context (e.g., during daily activities, in hospital environments). Given historical physiological data, different behavioral models tailored to specific conditions (e.g., a particular disease, a specific patient) are automatically learnt. A suitable model for the currently monitored patient is exploited in the real-time stream classification phase. The framework has been designed to perform both instantaneous evaluation and stream analysis over a sliding time window. To allow ubiquitous monitoring, real-time analysis could also be executed on mobile devices. As a case study, the framework has been validated in the intensive care scenario. Experimental validation, performed on 64 patients affected by different critical illnesses, demonstrates the effectiveness and the flexibility of the proposed framework in detecting different severity levels of monitored people's clinical situations.