Personalized and automated remote monitoring of atrial fibrillation. Rosier, A., Mabo, P., Temal, L., Van Hille, P., Dameron, O., Deléger, L., Grouin, C., Zweigenbaum, P., Jacques, J., Chazard, E., Laporte, L., Henry, C., & Burgun, A. Europace: European Pacing, Arrhythmias, and Cardiac Electrophysiology: Journal of the Working Groups on Cardiac Pacing, Arrhythmias, and Cardiac Cellular Electrophysiology of the European Society of Cardiology, 18(3):347–352, March, 2016.
Personalized and automated remote monitoring of atrial fibrillation [link]Paper  doi  abstract   bibtex   
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 atrial fibrillation (AF) alerts based on their medical significance. METHODS AND RESULTS: We evaluated this method on alerts for AF episodes that occurred in 60 pacemaker recipients. AKENATON prototype workflow includes two steps: natural language-processing algorithms abstract the patient 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 patient. Each alert is then automatically classified by importance from low to critical, by mimicking medical reasoning. Final classification was compared with human expert analysis by two physicians. A total of 1783 alerts about AF episode \textgreater5 min in 60 patients 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 patients. In particular, our method allows integration of patient 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 AF alerts in 60 patients, which resulted in an 84% reduction in notification workload, while preserving patient safety.
@article{rosier_personalized_2016,
	title = {Personalized and automated remote monitoring of atrial fibrillation},
	volume = {18},
	issn = {1532-2092},
	url = {https://hal-univ-rennes1.archives-ouvertes.fr/hal-01331019/document},
	doi = {10.1093/europace/euv234},
	abstract = {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 atrial fibrillation (AF) alerts based on their medical significance.
METHODS AND RESULTS: We evaluated this method on alerts for AF episodes that occurred in 60 pacemaker recipients. AKENATON prototype workflow includes two steps: natural language-processing algorithms abstract the patient 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 patient. Each alert is then automatically classified by importance from low to critical, by mimicking medical reasoning. Final classification was compared with human expert analysis by two physicians. A total of 1783 alerts about AF episode {\textgreater}5 min in 60 patients 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 patients. In particular, our method allows integration of patient 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 AF alerts in 60 patients, which resulted in an 84\% reduction in notification workload, while preserving patient safety.},
	language = {eng},
	number = {3},
	journal = {Europace: European Pacing, Arrhythmias, and Cardiac Electrophysiology: Journal of the Working Groups on Cardiac Pacing, Arrhythmias, and Cardiac Cellular Electrophysiology of the European Society of Cardiology},
	author = {Rosier, Arnaud and Mabo, Philippe and Temal, Lynda and Van Hille, Pascal and Dameron, Olivier and Deléger, Louise and Grouin, Cyril and Zweigenbaum, Pierre and Jacques, Julie and Chazard, Emmanuel and Laporte, Laure and Henry, Christine and Burgun, Anita},
	month = mar,
	year = {2016},
	pmid = {26487670},
	keywords = {Action Potentials, Algorithms, Anticoagulants, Artificial Intelligence, Artificial intelligence, Atrial Fibrillation, Atrial fibrillation, Automation, Cardiac implantable electronic devices, Decision Support Systems, Decision Support Techniques, Decision support systems, Electrocardiography, France, Heart Conduction System, Heart Rate, Humans, Pacemaker, Artificial, Pilot Projects, Predictive Value of Tests, Remote monitoring, Reproducibility of Results, Retrospective Studies, Risk Assessment, Signal Processing, Computer-Assisted, Telemetry, Workflow, Workload},
	pages = {347--352},
}
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