Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Online Mining (Recently) Maximal Frequent Itemsets over Data Streams
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
Finding Periodic Outliers over a Monogenetic Event Stream
UDM '05 Proceedings of the International Workshop on Ubiquitous Data Management
An Infrastructure of Stream Data Mining, Fusion and Management for Monitored Patients
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Anomaly detection scheme using data mining in mobile environment
ICCSA'03 Proceedings of the 2003 international conference on Computational science and its applications: PartII
Supporting the developers of context-aware mobile telemedicine applications
OTM'05 Proceedings of the 2005 OTM Confederated international conference on On the Move to Meaningful Internet Systems
An auto-stopped hierarchical clustering algorithm integrating outlier detection algorithm
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
Architecture of agent-based healthcare intelligent assistant on grid environment
PDCAT'04 Proceedings of the 5th international conference on Parallel and Distributed Computing: applications and Technologies
A secure mobile healthcare system using trust-based multicast scheme
IEEE Journal on Selected Areas in Communications - Special issue on wireless and pervasive communications for healthcare
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In this paper, we proposed a multi-agent based healthcare system (MAHS) which is the combination of a medical sensor module and wireless communication technology. This MAHS provides broad services for mobile telemedicine, patient monitoring, emergency management, doctor's diagnosis and prescription, patients and doctors and information exchange between hospital workers over a wide area. Futher more, MAHS is connected to a Body Area Network (BAN) and a doctor and hospital support staff. In this paper, we demonstrate how we can collect diagnosis patterns, classify them into normal, and emergency and be ready for an emergency by using the real-time biosignal data obtained from a patient's body. This proposed method deals with the enormous quantity of real-time sensing data and performs analysis and comparison between the data of patient's history and the real-time sensory data. In this paper, we separate Association rule exploration into two data groups: one is the existing enormous quantity of medical history data. The other group is real-time sensory data which is collected from sensors measuring body temperature, blood pressure, pulse. We suggest methods to analyze and model patterns of a patient's state for normal, and very emergency, and making decisions about a patient's present status by utilizing these two data groups.