Personalized and automated remote monitoring of atrial fibrillation.

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 >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.

Mapping BFO and DOLCE.

Upper level ontologies are key technology for integrating heterogeneous information coming from different sources. DOLCE and BFO, are the favorite candidates which propose rigorous foundational principles to model any domain. The objective of the AKENATON project is to improve alert management and to support patient-centered medical decision in telecardiology. This requires to integrate information transmitted by implantable cardiac devices with clinical data extracted from patient health records. To achieve this goal, we have designed an ontology of telecardiology based on DOLCE. In order to integrate ontologies based on BFO such as FMA, we have developed a framework for mapping BFO and DOLCE categories in terms of equivalence and subsumption between categories.

Towards an ontology for sharing medical images and regions of interest in neuroimaging.

The goal of the NeuroBase project is to facilitate collaborative research in neuroimaging through a federated system based on semantic web technologies. The cornerstone and focus of this paper is the design of a common semantic model providing a unified view on all data and tools to be shared. For this purpose, we built a multi-layered and multi-components formal ontology. This paper presents two major contributions. The first is related to the general methodology we propose for building an application ontology based on consistent conceptualization choices provided by the DOLCE foundational ontology and core ontologies of domains that we reuse; the second concerns the domain ontology we designed for neuroimaging, which encompasses both the objective nature of image data and the subjective nature of image content, through annotations based on regions of interest made by agents (humans or computer programs). We report on realistic domain use-case queries referring to our application ontology.

Remote Monitoring of Cardiac Implantable Devices: Ontology Driven Classification of the Alerts.

The number of patients that benefit from remote monitoring of cardiac implantable electronic devices, such as pacemakers and defibrillators, is growing rapidly. Consequently, the huge number of alerts that are generated and transmitted to the physicians represents a challenge to handle. We have developed a system based on a formal ontology that integrates the alert information and the patient data extracted from the electronic health record in order to better classify the importance of alerts. A pilot study was conducted on atrial fibrillation alerts. We show some examples of alert processing. The results suggest that this approach has the potential to significantly reduce the alert burden in telecardiology. The methods may be extended to other types of connected devices.

Automatic computation of CHA2DS2-VASc score: information extraction from clinical texts for thromboembolism risk assessment.

The CHA2DS2-VASc score is a 10-point scale which allows cardiologists to easily identify potential stroke risk for patients with non-valvular fibrillation. In this article, we present a system based on natural language processing (lexicon and linguistic modules), including negation and speculation handling, which extracts medical concepts from French clinical records and uses them as criteria to compute the CHA2DS2-VASc score. We evaluate this system by comparing its computed criteria with those obtained by human reading of the same clinical texts, and by assessing the impact of the observed differences on the resulting CHA2DS2-VASc scores. Given 21 patient records, 168 instances of criteria were computed, with an accuracy of 97.6%, and the accuracy of the 21 CHA2DS2-VASc scores was 85.7%. All differences in scores trigger the same alert, which means that system performance on this test set yields similar results to human reading of the texts.