面向区域医疗协同的可扩展远程医学教育系统设计与实现

在分析远程医学教育系统需求的基础上,提出构建可扩展远程医学教育系统和具体的系统架构;详细分析系统在前端、应用层、数据库层的可扩展性;以分层方式分析系统的部署模型;解释课件点播和实时交互两种培训模式的网络选型。最后说明可扩展远程医学教育系统的使用情况及社会效益。     

Joint source–channel coding: Secured and progressive transmission of compressed medical images on the Internet

The joint source–channel coding system proposed in this paper has two aims: lossless compression with a progressive mode and the integrity of medical data, which takes into account the priorities of the image and the properties of a network with no guaranteed quality of service. In this context, the use of scalable coding, locally adapted resolution (LAR) and a discrete and exact Radon transform, known as the Mojette transform, meets this twofold requirement. In this paper, details of this joint coding implementation are provided as well as a performance evaluation with respect to the reference CALIC coding and to unequal error protection using Reed–Solomon codes.     

基于结构化存储的HIS模型研究

医院信息系统是现代化医院建设中不可缺少的基础设施与支撑环境,是建设数字化医院的基础和支撑平台。本文就医院信息系统的定义和发展历程,提出了建立基于结构化存储的HIS模型的一些见解。     

Cellular computational networks—A scalable architecture for learning the dynamics of large networked systems

Neural networks for implementing large networked systems such as smart electric power grids consist of multiple inputs and outputs. Many outputs lead to a greater number of parameters to be adapted. Each additional variable increases the dimensionality of the problem and hence learning becomes a challenge. Cellular computational networks (CCNs) are a class of sparsely connected dynamic recurrent networks (DRNs). By proper selection of a set of input elements for each output variable in a given application, a DRN can be modified into a CCN which significantly reduces the complexity of the neural network and allows use of simple training methods for independent learning in each cell thus making it scalable. This article demonstrates this concept of developing a CCN using dimensionality reduction in a DRN for scalability and better performance. The concept has been analytically explained and empirically verified through application.     

ASSESSMENT OF BENZODIAZEPINE DEPENDENCE IN ALCOHOL AND DRUG DEPENDENT OUTPATIENTS: A RESEARCH REPORT

In this study on 99 outpatients who were being treated for alcohol and/or drug dependence and also using benzodiazepines (BZDs), prevalence rates of DSM-III-R and ICD-10 substance dependence diagnoses were ascertained and scalability, reliability and validity of the scales of the Benzodiazepine Dependence Self-Report Questionnaire (Bendep-SRQ) were assessed.The latter properties were investigated by Rasch analyses, discriminability coefficients, test–retest coefficients and factor analyses. BZD dependence was found to be a prevalent additional diagnosis. The psychometric findings appear to support the use of the Bendep-SRQ at outpatient addiction centers, which could contribute to a more differentiated treatment for poly-substance dependence.     

Unidimensionality and reliability under Mokken scaling of the Dutch language version of the SF-36

The sub-scales of the SF-36 in the Dutch National Study are investigated with respect to unidimensionality and reliability. It is argued that these properties deserve separate treatment. For unidimensionality we use a non-parametric model from item response theory, called the Mokken scaling model, and compute the corresponding scalability coefficients. We estimate reliability under the Mokken model, assuming that the items are double homogeneous, and compare it to Cronbach's . The scalability of the sub-scale general health perceptions is medium (H = 0.46), and for the other sub-scales it is strong (H 0.6). The reliability in terms of indicates that all sub-scales can be used in basic research ( > 0.70), but that only physical functioning can be used for clinical applications of quality of life ( > 0.90). The relative merits of our approach are discussed.     

Beowulf-T机群系统高可扩展性的研究

并行计算技术是衡量一个国家科技水平的重要标志之一,PC机群计算机是最廉价的高性能计算机.本文构建了一个Beowulf-T机群系统,提出了在该系统上的加速比和效率计算公式.通过实例的测试说明该机群系统具有高可扩展性.     

Simultaneous trend analysis for evaluating outcomes in patient-centred health monitoring services

The research aim underpinning the Healthcare@Home (HH) information system described here was to enable ‘near real time’ risk analysis for disease early detection and prevention. To this end, we are implementing a family of prototype web services to ‘push’ or ‘pull’ individual’s health-related data via an system of clinical hubs, mobile communication devices and/or dedicated home-based network computers. We are examining more efficient methods for ethical use of such data in timeline-based (i.e. ‘longitudinal’) data analysis systems. A consistent data collation infrastructure is being created for use along the ‘patient path’—accessible wherever patients happen to be. This ‘patient-centred’ infrastructure can be applied in the evaluation of disease progression risk (in the light of clinical understanding of disease processes). In this paper we describe the requirements for making multi-data trend management ‘scale-up’, together with some requirements of an ‘end-to-end’ functioning data collection system. A Service-Oriented Architecture (SOA) approach is used to maximise benefits from (1) clinical evidence and (2) computational models of disease progression that can be made available elsewhere on the SOA. We discuss the implications of this so-called ‘closed loop’ approach for improving healthcare intervention outcomes, patient safety, decision support, objective measurement of service quality and in providing inputs for quantitative healthcare (predictive) modelling. To aid understanding, a concise glossary is provided for italicized technical or common ‘jargon’ terms that are not defined in the text.