Recent advancements in science and technology,coupled with the proliferation of data,have also urged laboratory medicine to integrate with the era of artificial intelligence(AI)and machine learning(ML).In the current ...Recent advancements in science and technology,coupled with the proliferation of data,have also urged laboratory medicine to integrate with the era of artificial intelligence(AI)and machine learning(ML).In the current practices of evidencebased medicine,the laboratory tests analysing disease patterns through the association rule mining(ARM)have emerged as a modern tool for the risk assessment and the disease stratification,with the potential to reduce cardiovascular disease(CVD)mortality.CVDs are the well recognised leading global cause of mortality with the higher fatality rates in the Indian population due to associated factors like hypertension,diabetes,and lifestyle choices.AI-driven algorithms have offered deep insights in this field while addressing various challenges such as healthcare systems grappling with the physician shortages.Personalized medicine,well driven by the big data necessitates the integration of ML techniques and high-quality electronic health records to direct the meaningful outcome.These technological advancements enhance the computational analyses for both research and clinical practice.ARM plays a pivotal role by uncovering meaningful relationships within databases,aiding in patient survival prediction and risk factor identification.AI potential in laboratory medicine is vast and it must be cautiously integrated while considering potential ethical,legal,and privacy concerns.Thus,an AI ethics framework is essential to guide its responsible use.Aligning AI algorithms with existing lab practices,promoting education among healthcare professionals,and fostering careful integration into clinical settings are imperative for harnessing the benefits of this transformative technology.展开更多
为解决传统通信运营商业务集中统一规则部署,业务无法灵活差异化定制以适应5G To B垂直行业多样化业务需求的问题,提出了一种基于AI智能关联算法的5G网络切片现网实现方案。首先借助对5G多域多接口数据采集形成数据分析基础,从不同垂直...为解决传统通信运营商业务集中统一规则部署,业务无法灵活差异化定制以适应5G To B垂直行业多样化业务需求的问题,提出了一种基于AI智能关联算法的5G网络切片现网实现方案。首先借助对5G多域多接口数据采集形成数据分析基础,从不同垂直行业客户SLA需求给出网络切片的模型选择;然后借助二分K均值聚类AI算法寻找业务流在各环节的最佳切片参数,指导运营商为垂直行业客户提供端到端最优切片参数集;最后基于该算法给出了现网热门的5G应用如8K视频、高清在线游戏运用智能AI算法后的切片效率及性能质量的对比,为运营商5G网络切片技术在现网的应用实践提供参考。展开更多
文摘Recent advancements in science and technology,coupled with the proliferation of data,have also urged laboratory medicine to integrate with the era of artificial intelligence(AI)and machine learning(ML).In the current practices of evidencebased medicine,the laboratory tests analysing disease patterns through the association rule mining(ARM)have emerged as a modern tool for the risk assessment and the disease stratification,with the potential to reduce cardiovascular disease(CVD)mortality.CVDs are the well recognised leading global cause of mortality with the higher fatality rates in the Indian population due to associated factors like hypertension,diabetes,and lifestyle choices.AI-driven algorithms have offered deep insights in this field while addressing various challenges such as healthcare systems grappling with the physician shortages.Personalized medicine,well driven by the big data necessitates the integration of ML techniques and high-quality electronic health records to direct the meaningful outcome.These technological advancements enhance the computational analyses for both research and clinical practice.ARM plays a pivotal role by uncovering meaningful relationships within databases,aiding in patient survival prediction and risk factor identification.AI potential in laboratory medicine is vast and it must be cautiously integrated while considering potential ethical,legal,and privacy concerns.Thus,an AI ethics framework is essential to guide its responsible use.Aligning AI algorithms with existing lab practices,promoting education among healthcare professionals,and fostering careful integration into clinical settings are imperative for harnessing the benefits of this transformative technology.
文摘为解决传统通信运营商业务集中统一规则部署,业务无法灵活差异化定制以适应5G To B垂直行业多样化业务需求的问题,提出了一种基于AI智能关联算法的5G网络切片现网实现方案。首先借助对5G多域多接口数据采集形成数据分析基础,从不同垂直行业客户SLA需求给出网络切片的模型选择;然后借助二分K均值聚类AI算法寻找业务流在各环节的最佳切片参数,指导运营商为垂直行业客户提供端到端最优切片参数集;最后基于该算法给出了现网热门的5G应用如8K视频、高清在线游戏运用智能AI算法后的切片效率及性能质量的对比,为运营商5G网络切片技术在现网的应用实践提供参考。
