As service oriented architecture （SOA） matures, service consumption demand leads to an urgent requirement for service discovery. Unlike Web documents, services are intended to be executed to achieve objectives and/o...As service oriented architecture （SOA） matures, service consumption demand leads to an urgent requirement for service discovery. Unlike Web documents, services are intended to be executed to achieve objectives and/or desired goals of users. This leads to the notion that service discovery should take the ＂usage context＂ of service into account as well as service content （descriptions） which have been well explored. In this paper, we introduce the concept of service context which is used to represent service usage. In query processing, both service content and service context are ex- amined to identify services. We propose to represent ser- vice context by a weighted bipartite graph model. Based on the bipartite graph model, we reduce the gap between query space and service space by query expansion to improve re- call. We also design an iteration algorithm for result ranking by considering service contextsefulness as well as contentrelevance to improve precision. Finally, we develop a service search engine implementing this mechanism, and conduct some experiments to verify our idea.展开更多
This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation‐wide screening programs.The first example is embedded within the setting of a nat...This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation‐wide screening programs.The first example is embedded within the setting of a national level population health screening program for diabetes related eye diseases,targeting the rapidly increasing number of adults in the country with diabetes.In the second example,the AI assisted screening is done shortly after a person is admitted to one of the public hospitals to identify which inpatients—especially which elderly patients with complex conditions—have a high risk of being readmitted as an inpatient multiple times in the months following discharge.Ways in which healthcare needs and the clinical operations context influenced the approach to designing or deploying the AI systems are highlighted,illustrating the multiplicity of factors that shape the requirements for successful large‐scale deployments of AI systems that are deeply embedded within clinical workflows.In the first example,the choice was made to use the system in a semi‐automated(vs.fully automated)mode as this was assessed to be more cost‐effective,though still offering substantial productivity improvement.In the second example,machine learning algorithm design and model execution trade-offs were made that prioritized key aspects of patient engagement and inclusion over higher levels of predictive accuracy.The article concludes with several lessons learned related to deploying AI systems within healthcare settings,and also lists several other AI efforts already in deployment and in the pipeline for Singapore's public healthcare system.展开更多
文摘As service oriented architecture （SOA） matures, service consumption demand leads to an urgent requirement for service discovery. Unlike Web documents, services are intended to be executed to achieve objectives and/or desired goals of users. This leads to the notion that service discovery should take the ＂usage context＂ of service into account as well as service content （descriptions） which have been well explored. In this paper, we introduce the concept of service context which is used to represent service usage. In query processing, both service content and service context are ex- amined to identify services. We propose to represent ser- vice context by a weighted bipartite graph model. Based on the bipartite graph model, we reduce the gap between query space and service space by query expansion to improve re- call. We also design an iteration algorithm for result ranking by considering service contextsefulness as well as contentrelevance to improve precision. Finally, we develop a service search engine implementing this mechanism, and conduct some experiments to verify our idea.
文摘This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation‐wide screening programs.The first example is embedded within the setting of a national level population health screening program for diabetes related eye diseases,targeting the rapidly increasing number of adults in the country with diabetes.In the second example,the AI assisted screening is done shortly after a person is admitted to one of the public hospitals to identify which inpatients—especially which elderly patients with complex conditions—have a high risk of being readmitted as an inpatient multiple times in the months following discharge.Ways in which healthcare needs and the clinical operations context influenced the approach to designing or deploying the AI systems are highlighted,illustrating the multiplicity of factors that shape the requirements for successful large‐scale deployments of AI systems that are deeply embedded within clinical workflows.In the first example,the choice was made to use the system in a semi‐automated(vs.fully automated)mode as this was assessed to be more cost‐effective,though still offering substantial productivity improvement.In the second example,machine learning algorithm design and model execution trade-offs were made that prioritized key aspects of patient engagement and inclusion over higher levels of predictive accuracy.The article concludes with several lessons learned related to deploying AI systems within healthcare settings,and also lists several other AI efforts already in deployment and in the pipeline for Singapore's public healthcare system.
