Personalized and automated remote monitoring of XXXX .

@entity1 @entity955
AIMS: Remote monitoring of cardiac implantable electronic devices is a growing standard; yet, remote follow-up and management of alerts represents a time-consuming task for physicians or trained staff. This study evaluates an automatic mechanism based on artificial intelligence tools to filter @entity955 ( @entity955 ) alerts based on their medical significance. METHODS AND RESULTS: We evaluated this method on alerts for @entity955 episodes that occurred in 60 pacemaker recipients. AKENATON prototype workflow includes two steps: natural language-processing algorithms abstract the @entity1 health record to a digital version, then a knowledge-based algorithm based on an applied formal ontology allows to calculate the CHA2DS2-VASc score and evaluate the anticoagulation status of the @entity1 . Each alert is then automatically classified by importance from low to critical, by mimicking medical reasoning. Final classification was compared with @entity1 expert analysis by two physicians. A total of 1783 alerts about @entity955 episode >5 min in 60 @entity1 were processed. A 1749 of 1783 alerts (98%) were adequately classified and there were no underestimation of alert importance in the remaining 34 misclassified alerts. CONCLUSION: This work demonstrates the ability of a pilot system to classify alerts and improves personalized remote monitoring of @entity1 . In particular, our method allows integration of @entity1 medical history with device alert notifications, which is useful both from medical and resource-management perspectives. The system was able to automatically classify the importance of 1783 @entity955 alerts in 60 @entity1 , which resulted in an 84% reduction in notification workload, while preserving @entity1 safety.
