目的分析2009~2024年间国际延时现场救护领域的文献,探究主要研究主题及其发展趋势,以期为未来救护策略提供理论支持。方法系统检索PubMed、Embase、Web of Science和中国知网等数据库,筛选并纳入283篇相关文献。运用BERTopic主题建模...目的分析2009~2024年间国际延时现场救护领域的文献,探究主要研究主题及其发展趋势,以期为未来救护策略提供理论支持。方法系统检索PubMed、Embase、Web of Science和中国知网等数据库,筛选并纳入283篇相关文献。运用BERTopic主题建模技术对文献进行主题识别和关键词分析,并进行可视化展示。结果当前研究主要聚焦在“急救策略研究”“智能技术与信息管理”“实战应用”与“政策与理论研究”等4个方面,预测这些领域将持续成为研究热点。结论国际延时现场救护研究正处于快速发展阶段,建议未来研究深入重点领域,开发有效的救护策略,以提升救治效率和伤员生存率。展开更多
Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper, we propose a user participation behavior prediction model for social hotspots, based on user ...Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper, we propose a user participation behavior prediction model for social hotspots, based on user behavior and relationship data, to predict user participation behavior and topic development trends. Firstly, for the complex factors of user behavior, three dynamic influence factor functions are defined, including individual, peer and community influence. These functions take timeliness into account using a time discretization method. Secondly, to determine laws of individual behavior and group behavior within a social topic, a hotspot user participation behavior prediction model is proposed and associated with the basic concepts of randora field and Markov property in information diffusion. The experimental results show that the model can not only dynamically predict the individual behavior, but also grasp the development trends of topics.展开更多
文摘目的分析2009~2024年间国际延时现场救护领域的文献,探究主要研究主题及其发展趋势,以期为未来救护策略提供理论支持。方法系统检索PubMed、Embase、Web of Science和中国知网等数据库,筛选并纳入283篇相关文献。运用BERTopic主题建模技术对文献进行主题识别和关键词分析,并进行可视化展示。结果当前研究主要聚焦在“急救策略研究”“智能技术与信息管理”“实战应用”与“政策与理论研究”等4个方面,预测这些领域将持续成为研究热点。结论国际延时现场救护研究正处于快速发展阶段,建议未来研究深入重点领域,开发有效的救护策略,以提升救治效率和伤员生存率。
基金supported by the National Key Basic Research Program(973 program)of China(No.2013CB329606)National Science Foundation of China(Grant No.61272400)+2 种基金Science and Technology Research Program of the Chongqing Municipal Education Committee(No.KJ1500425)Wen Feng Foundation of CQUPT(No.WF201403)Chongqing Graduate Research And Innovation Project(No.CYS14146)
文摘Hotspot topic trends can be captured by analyzing user attributes and historical behavior in social network. In this paper, we propose a user participation behavior prediction model for social hotspots, based on user behavior and relationship data, to predict user participation behavior and topic development trends. Firstly, for the complex factors of user behavior, three dynamic influence factor functions are defined, including individual, peer and community influence. These functions take timeliness into account using a time discretization method. Secondly, to determine laws of individual behavior and group behavior within a social topic, a hotspot user participation behavior prediction model is proposed and associated with the basic concepts of randora field and Markov property in information diffusion. The experimental results show that the model can not only dynamically predict the individual behavior, but also grasp the development trends of topics.