Recent impact study of automatic search function of the pacemaker atrial hysteresis on atrial arrhythmias

Objective:To explore the recent influences of pacemaker with automatic search function of atrial hysteresis on atrial arrhythmias,and to evaluate it clinical efficacy and safety.Methods:Indentify ADx DDD 5286 implanted dual chamber pacemaker for sick sinus syndrome in 43 cases.Automatic search of atrial lag was not opened with after pacemaker implantation,and the pacemaker settings were kept.Follow-up program after 3 months,DDD mode with automatic search of atrial lag was opened,and this mode was followed up for 6 months,comparing the atrial pacing percentage and DCG atrial tachyarrhythmias of pacemaker implantation to opening atrium lag mode.Results:Compared with the preoperative and operative 3 months later,dynamic electrocardiogram(DCG)24 h showed that the number of atrial premature beats(APB)and atrial tachycardia,atrial fibrillation(AF)array increased(p<.05);the cases of APB,atrial tachycardia and AF episodes were also increased(p<.05).Compared with the automatic search function in atrial hysteresis model opened with and not opened:atrial pacing percentage decreased[0.54(0.41,0.71)vs.0.82(0.65,0.93),p<.05];DCG 24 h showed that the number of APB,AF episodes was reduced(p<.05).Conclusions:Automation search function in atrial hysteresis model can obviously reduce the proportion of atrial pacing,reduce the occurrence of atrial arrhythmias;opened with automatic search function in atrial hysteresis model was safe and reliable.

Knowledge Graph Representation Learning Based on Automatic Network Search for Link Prediction

Link prediction,also known as Knowledge Graph Completion(KGC),is the common task in Knowledge Graphs(KGs)to predict missing connections between entities.Most existing methods focus on designing shallow,scalable models,which have less expressive than deep,multi-layer models.Furthermore,most operations like addition,matrix multiplications or factorization are handcrafted based on a few known relation patterns in several wellknown datasets,such as FB15k,WN18,etc.However,due to the diversity and complex nature of real-world data distribution,it is inherently difficult to preset all latent patterns.To address this issue,we proposeKGE-ANS,a novel knowledge graph embedding framework for general link prediction tasks using automatic network search.KGEANS can learn a deep,multi-layer effective architecture to adapt to different datasets through neural architecture search.In addition,the general search spacewe designed is tailored forKGtasks.We performextensive experiments on benchmark datasets and the dataset constructed in this paper.The results show that our KGE-ANS outperforms several state-of-the-art methods,especially on these datasets with complex relation patterns.