Event Prediction with Dynamic Knowledge Base on Health Care Data

Abstract

The association rule mining techniques are used to detect activities from data sets. Event detection refers to an action taken to an activity. The gap between the actual event and event notification should be minimized. Event derivation should also scale for a large number of complex rules. Attacks and its severity are identified from event derivation systems. Transactional databases and external data sources are used. Event detection system identifies the new events in uncertainty environment. Relevance estimation is a more challenging task under uncertain event analysis. Selectability and sampling mechanism are used to improve the derivation accuracy. A Bayesian network representation is used to derive new events given the arrival of an uncertain event and to compute its probability. The event derivation system is enhanced to map dynamic rules on uncertain data environment. Rule probability estimation is carried out using the apriori algorithm. The rule derivation process is optimized for domain specific model.