An Open Invitation to Join the International Brugada Electrocardiographic Indices Registry

Background:The Brugada Electrocardiographic Indices Registry is a comprehensive data registry composed of patients with Brugada patterns on the electrocardiogram(ECG).The aim is to test the hypotheses that(i)ECG indices combining both depolarization and repolarization abnormalities can better predict spontaneous ventricular arrhythmias than existing ECG markers in Brugada syndrome and(ii)that serial ECG measurements will provide additional information for risk stratifi cation,especially in asymptomatic patients.Methods:Patients with both Brugada pattern ECGs and Brugada syndrome are eligible for inclusion in this registry.Baseline characteristics and ECG variables refl ecting depolarization and repolarization will be determined.The primary outcome is spontaneous ventricular tachycardia/ventricular fi brillation or sudden cardiac death.Secondary outcomes are inducible ventricular tachycardia/ventricular fi brillation and syncope.Results:As of November 15,2019,39 investigators from 32 cities in 18 countries had joined this registry.As of December 15,2019,1383 cases had been enrolled.Conclusions:The Brugada Electrocardiographic Indices Registry will evaluate the disease life course,risk factors,and prognosis in a large series of Brugada patients.It will therefore provide insights for improving risk stratification.

Semantic Similarity between Ontologies at Different Scales

In the past decade, existing and new knowledge and datasets have been encoded in different ontologies for semantic web and biomedical research. The size of ontologies is often very large in terms of number of concepts and relationships, which makes the analysis of ontologies and the represented knowledge graph computational and time consuming. As the ontologies of various semantic web and biomedical applications usually show explicit hierarchical structures, it is interesting to explore the trade-offs between ontological scales and preservation/precision of results when we analyze ontologies. This paper presents the first effort of examining the capability of this idea via studying the relationship between scaling biomedical ontologies at different levels and the semantic similarity values. We evaluate the semantic similarity between three gene ontology slims(plant,yeast, and candida, among which the latter two belong to the same kingdom — fungi) using four popular measures commonly applied to biomedical ontologies(Resnik, Lin, Jiang-Conrath,and Sim Rel). The results of this study demonstrate that with proper selection of scaling levels and similarity measures, we can significantly reduce the size of ontologies without losing substantial detail. In particular, the performances of JiangConrath and Lin are more reliable and stable than that of the other two in this experiment, as proven by 1) consistently showing that yeast and candida are more similar(as compared to plant) at different scales, and 2) small deviations of the similarity values after excluding a majority of nodes from several lower scales.This study provides a deeper understanding of the application of semantic similarity to biomedical ontologies, and shed light on how to choose appropriate semantic similarity measures for biomedical engineering.

On social computing research collaboration patterns： a social network perspective

社会计算的领域超过十年以前出现了。在最后十年期间，从许多学科的研究人员仔细一直在协作增加社会计算研究的生长。这篇论文瞄准识别关键研究人员和机构，并且在这个领域里检验合作模式。我们在不同层次采用合作作家身分网络分析从 1998 ～ 2011 在社会计算学习 6 543 份出版物的文献的信息。这篇论文在社会计算和罐头给当前的研究的一张快照在社会计算提供起始的指导给新研究人员。

The Chinese ‘‘HumanFlesh’’ Web:the first decade and beyond

Human flesh search(HFS), a Web-enabled crowdsourcing phenomenon, originated in China a decade ago. In this article, we present the first comprehensive empirical analysis of HFS, focusing on the scope of HFS activities, the patterns of HFS crowd collaboration process, and the characteristics of HFS participant networks. A survey of HFS participants was conducted to provide an in-depth understanding of the HFS community and various factors that motivate these participants to contribute. This article also advocates a new stream of Web science and social computing research that will be important in predicting the future growth and use of the World Wide Web.

Constructing public health evidence knowledge graph for decision-making support from COVID-19 literature of modelling study

The needs of mitigating COVID-19 epidemic prompt policymakers to make public health-related decision under the guidelines of science.Tremendous unstructured COVID-19 publications make it challenging for policymakers to obtain relevant evidence.Knowledge graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making recently.Here,we introduce a novel framework that can ex-tract the COVID-19 public health evidence knowledge graph(CPHE-KG)from papers relating to a modelling study.We screen out a corpus of 3096 COVID-19 modelling study papers by performing a literature assessment process.We define a novel annotation schema to construct the COVID-19 modelling study-related IE dataset(CPHIE).We also propose a novel multi-tasks document-level information extraction model SS-DYGIE++based on the dataset.Leveraging the model on the new corpus,we construct CPHE-KG containing 60,967 entities and 51,140 rela-tions.Finally,we seek to apply our KG to support evidence querying and evidence mapping visualization.Our SS-DYGIE++(SpanBERT)model has achieved a F1 score of 0.77 and 0.55 respectively in document-level entity recognition and coreference resolution tasks.It has also shown high performance in the relation identification task.With evidence querying,our KG can present the dynamic transmissions of COVID-19 pandemic in different countries and regions.The evidence mapping of our KG can show the impacts of variable non-pharmacological interventions to COVID-19 pandemic.Analysis demonstrates the quality of our KG and shows that it has the potential to support COVID-19 policy making in public health.

Adaptively temporal graph convolution model for epidemic prediction of multiple age groups

Introduction:Multivariate time series prediction of infectious diseases is significant to public health,and the deep learning method has attracted increasing attention in this research field.Material and methods:An adaptively temporal graph convolution(ATGCN)model,which leams the contact patterns of multiple age groups in a graph-based approach,was proposed for COVID-19 and influenza prediction.We compared ATGCN with autoregressive models,deep sequence learning models,and experience-based ATGCN models in short-term and long-term prediction tasks.Results:Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term(12.5%and 10%improvements on RMSE)and longterm(12.4%and 5%improvements on RMSE)prediction tasks.And the RMSE of ATGCN predictions fluctuated least in different age groups of COVID-19(0.029±0.003)and influenza(0.059±0.008).Compared with the Ones-ATGCN model or the Pre-ATGCN model,the ATGCN model was more robust in performance,with RMSE of 0.0293 and 0.06 on two datasets when horizon is one.Discussion:Our research indicates a broad application prospect of deep learning in the field of infectious disease prediction.Transmission characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature selection.Conclusion:The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups,indicating its great potentials for exploring the implicit interactions of multivariate variables.

Investigating the effect of online and offline reputation on the provision of online counseling services: A case study of the Internet hospitals in China

The sustainable development of Internet hospitals and e-health platforms relies on the participation of patients and physicians,especially on the provision of health counseling services by physicians.The objective of our study is to explore the factors motivating Chinese physicians to provide online health counseling services from the perspectives of their online and offline reputation.We collect the data of 141029 physicians from 6173 offline hospitals located in 350 cities in China.Based on the reputation theory and previous studies,we incorporate patients’feedback as physicians’online reputation and incorporate physicians’offline professional status as physicians’offline reputation.Results show that physicians’online reputation significantly and positively influence their online counseling behaviors,whereas physicians’offline reputation significantly and negatively influence their online counseling behaviors.We conclude that physician’s online and offline reputations show a competitive and substitute relationship rather than a complementary relationship in influencing physicians to provide online counseling services in Internet hospitals.One possible explanation for the substitute relationship could be the constraints of limited time and effort of physicians.

Special issue: Innovative applications of big data and artificial intelligence

Big data refers to complex data sets that cannot be processed using traditional data processing techniques.It could also be defined as a large amount of unstructured or structured data from various sources.Artificial intelligence(AI)refers to the science of expressing and acquiring knowledge using data,algorithms,and computing powers.So far,the big data industry chain is gradually mature,leading to a combination of big data and AI techniques developed in comprehensive directions.The cutting-edge technologies of big data and AI have played an increasingly important role in the innovative development of various industries such as epidemic prevention and control,emergency management,social governance,manufacturing,medical services,intelligent transportation,Internet of Things/supply chain,Internet industry,new media,and so on.In addition,there are many typical innovative projects,which are being produced.