目的:设计一种基于服务对象与医院科室之间的个案管理信息化系统,帮助肿瘤患者或高危人群建立持续的电子健康档案,实现个体化的预防、筛查、咨询、诊疗和康复指导,并提供便捷的医患沟通平台。方法:采用B/S及多层体系结构并存的结构模型...目的:设计一种基于服务对象与医院科室之间的个案管理信息化系统,帮助肿瘤患者或高危人群建立持续的电子健康档案,实现个体化的预防、筛查、咨询、诊疗和康复指导,并提供便捷的医患沟通平台。方法:采用B/S及多层体系结构并存的结构模型,应用NET+RDBMS+Web Service等的技术路线,数据库系统采用SQL Server 2005,数据集采用国家卫生部的肿瘤病例管理行业标准(《WS 372.6—2012》)等自主设计开发。结果:肿瘤防治个案管理的信息化平台实现了电子化资料存储和自动分类,肿瘤患者或高危人群可以通过外网(互联网)和医院内网的PC工作站及时掌握自己的健康信息和诊疗资料,并可以通过系统的医患沟通平台与医护人员进行个体化交流。系统应用实现了预期目标,改善了患者或高危人群的就医体验,提高了服务对象的满意度和随访率。结论:该系统实现了肿瘤防治个案管理模式的信息化,在肿瘤防治单位具有推广应用价值。展开更多
The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class （HLC） models. A similar effort was made earlier by Zhang （200...The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class （HLC） models. A similar effort was made earlier by Zhang （2002）, but that study involved only small applications with 4 or 5 observed variables and no more than 2 latent variables due to the lack of efficient learning algorithms. Significant progress has been made since then on algorithmic research, and it is now possible to learn HLC models with dozens of observed variables. This allows us to demonstrate the benefits of HLC models more convincingly than before. The authors have successfully analyzed the CoIL Challenge 2000 data set using HLC models. The model obtained consists of 22 latent variables, and its structure is intuitively appealing. It is exciting to know that such a large and meaningful latent structure can be automatically inferred from data.展开更多
文摘目的:设计一种基于服务对象与医院科室之间的个案管理信息化系统,帮助肿瘤患者或高危人群建立持续的电子健康档案,实现个体化的预防、筛查、咨询、诊疗和康复指导,并提供便捷的医患沟通平台。方法:采用B/S及多层体系结构并存的结构模型,应用NET+RDBMS+Web Service等的技术路线,数据库系统采用SQL Server 2005,数据集采用国家卫生部的肿瘤病例管理行业标准(《WS 372.6—2012》)等自主设计开发。结果:肿瘤防治个案管理的信息化平台实现了电子化资料存储和自动分类,肿瘤患者或高危人群可以通过外网(互联网)和医院内网的PC工作站及时掌握自己的健康信息和诊疗资料,并可以通过系统的医患沟通平台与医护人员进行个体化交流。系统应用实现了预期目标,改善了患者或高危人群的就医体验,提高了服务对象的满意度和随访率。结论:该系统实现了肿瘤防治个案管理模式的信息化,在肿瘤防治单位具有推广应用价值。
基金Hong Kong Grants Council Grants #622105 and #622307the National Basic Research Program of China (aka the 973 Program) under project No.2003CB517106.
文摘The authors present a case study to demonstrate the possibility of discovering complex and interesting latent structures using hierarchical latent class （HLC） models. A similar effort was made earlier by Zhang （2002）, but that study involved only small applications with 4 or 5 observed variables and no more than 2 latent variables due to the lack of efficient learning algorithms. Significant progress has been made since then on algorithmic research, and it is now possible to learn HLC models with dozens of observed variables. This allows us to demonstrate the benefits of HLC models more convincingly than before. The authors have successfully analyzed the CoIL Challenge 2000 data set using HLC models. The model obtained consists of 22 latent variables, and its structure is intuitively appealing. It is exciting to know that such a large and meaningful latent structure can be automatically inferred from data.
