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تسجيل مستخدم جديدModel Checking Implantable Cardioverter Defibrillators
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Ventricular Fibrillation is a disorganized electrical excitation of the heart that results in inadequate blood flow to the body. It usually ends in death within seconds. The most common way to treat the symptoms of fibrillation is to implant a medical device, known as an Implantable Cardioverter Defibrillator (ICD), in the patients body. Model-based verification can supply rigorous proofs of safety and efficacy. In this paper, we build a hybrid system model of the human heart+ICD closed loop, and show it to be a STORMED system, a class of o-minimal hybrid systems that admit finite bisimulations. In general, it may not be possible to compute the bisimulation. We show that approximate reachability can yield a finite simulation for STORMED systems, which improves on the existing verification procedure. In the process, we show that certain compositions respect the STORMED property. Thus it is possible to model check important formal properties of ICDs in a closed loop with the heart, such as delayed therapy, missed therapy, or inappropriately administered therapy. The results of this paper are theoretical and motivate the creation of concrete model checking procedures for STORMED systems.
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Background: One of the common causes of sudden cardiac death (SCD) in young people is hypertrophic cardiomyopathy (HCM) and the primary prevention of SCD is with an implantable cardioverter defibrillators (ICD). Concerning the incidence of appropriat
e ICD therapy and the complications associated with ICD implantation and discharge, patients with implanted ICDs are closely monitored and interrogation reports are generated from clinical consultations. Methods: In this study, we compared the performance of structured device data and unstructured interrogation reports for extracting information of ICD therapy and heart rhythm. We sampled 687 reports with a gold standard generated through manual chart review. A rule-based natural language processing (NLP) system was developed using 480 reports and the information in the corresponding device data was aggregated for the task. We compared the performance of the NLP system with information aggregated from structured device data using the remaining 207 reports. Results: The rule-based NLP system achieved F-measure of 0.92 and 0.98 for ICD therapy and heart rhythm while the performance of aggregating device data was significantly lower with F-measure of 0.78 and 0.45 respectively. Limitations of using only structured device data include no differentiation of real events from management events, data availability, and disparate perspectives of vendor and data granularity while using interrogation reports needs to overcome non-representative keyword/pattern and contextual errors. Conclusions: Extracting phenotyping information from data generated in real-world requires the incorporation of medical knowledge. It is essential to analyze, compare, and harmonize multiple data sources for real-world evidence generation.
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