“军卫一号”数据仓库建设中数据质量问题的研究

在数据仓库建设过程中，数据仓库中数据质量问题常常被人忽略。本文对“军卫一号”数据仓库主题对应相关业务表的数据质量进行了分析，给出了不良数据质量原因分析，提出了解决办法。     

数据仓库及其在医学领域中的应用

本文介绍了数据仓库和数据集市的基本概念，并通过数据仓库在医学领域中的应用，说明了创建数据仓库的一般方法。     

基于数据仓库实现疾病决策支持辅助信息系统

由于数据仓库已成为决策支持系统的关键，为决策支持系统在医疗卫生领域的发展和全面应用提供了理论基础，本文指出应当采取数据仓库技术来实现疾病决策支持辅助信息系统，从而完善现有的医院信息系统，加快区域性医疗系统建设。并且论述了采用数据仓库实现这个系统的数据基础的三个主要原因，同时指出如何构建数据仓库，以及为了更好地实现目标而采用数据集市的数据仓库体系结构。     

数据仓库技术及其在医院信息管理中的应用

医院信息系统的建立，为各级医疗单位提供了大量的数据，但却提供有限的管理决策信息。要在大量的医疗数据中进行有效的分析。了解医疗业务的发展情况。这就要充分利用数据仓库技术来进行数据组织、存储和分析。本文分别就数据仓库的定义、设计、体系结构以及不同厂家的数据仓库解决方案等进行阐述。特别是对数据进入数据仓库的主要过程加以说明，并以住院收费分析为例。论述了数据仓库在决策支持上的作用。     

数据仓库技术在医院管理及决策中的应用

本文介绍了数据仓库技术的基本概念，对本院数据仓库的建立进行了尝试，并指出医院数据仓库系统的主要功能及应用前景。     

数据仓库技术及在医院的应用

数据仓库是适应决策支持的需要，在数据库的基础上发展起来的信息技术，具有广阔的应用前景。本文介绍了数据仓库等数据分析工具的基本概念，并对数据仓库在医院中的应用进行了初步探讨。     

利用SQLServer2005构建病案统计系统

解读Microsoft SQL Server 2005技术平台构建数据仓库中的技术要点,揭示广东省统计病案管理系统服务器的数据导入、激活方法以及客户端连接技术要求,并讨论病案统计软件数据的各种备份、恢复方法.强调了多层次、高稳定的数据仓库技术对增强医院决策和合理配置医疗资源的积极作用.     

历史数据的维护及利用

医院信息系统经过几年的实际运行，积累了大量历史数据。这些历史数据使用频率很低，又对系统性能产生影响。目前处理这些数据的方法主要是以清除、保留和查询为主，历史数据很少得到再利用。本文在处理历史数据的过程中，与数据仓库技术结合起来，使整个处理过程清晰，同时从多层面对历史数据进行分析，有效提高医院的数据利用率和信息处理能力，还能为医院决策者们提供有效的决策支持。     

专业图书馆开发、应用数据仓库技术初步设想

本文简单介绍了数据仓库技术的概念、特点，并就数据仓库技术对提升专业图书馆采购工作科学性、增加检索、查新工作科技含量进行了论述，并就专业图书馆如何应用、开发数据仓库技术以及实际存在的问题进行了探讨．     

用SQL Server2000构建医院数据仓库的尝试

本文分析了数据仓库的特点、体系机构及实现的方法，并以SQL Server2000作为数据库的支持平台，探索数据仓库在医院的应用。     

DW4TR: A Data Warehouse for Translational Research

The linkage between the clinical and laboratory research domains is a key issue in translational research. Integration of clinicopathologic data alone is a major task given the number of data elements involved. For a translational research environment, it is critical to make these data usable at the point-of-need. Individual systems have been developed to meet the needs of particular projects though the need for a generalizable system has been recognized. Increased use of Electronic Medical Record data in translational research will demand generalizing the system for integrating clinical data to support the study of a broad range of human diseases. To ultimately satisfy these needs, we have developed a system to support multiple translational research projects. This system, the Data Warehouse for Translational Research (DW4TR), is based on a light-weight, patient-centric modularly-structured clinical data model and a specimen-centric molecular data model. The temporal relationships of the data are also part of the model. The data are accessed through an interface composed of an Aggregated Biomedical-Information Browser (ABB) and an Individual Subject Information Viewer (ISIV) which target general users. The system was developed to support a breast cancer translational research program and has been extended to support a gynecological disease program. Further extensions of the DW4TR are underway. We believe that the DW4TR will play an important role in translational research across multiple disease types.     

基于神经网络的心电数据压缩

针对动态心电监护系统中心电监护数据量很大,其中只有一部分特征才对诊断有意义,本文提出了一种基于神经网络的心电数据压缩算法。该算法根据ECG特征的变化,自动调整神经网络的结构参数,从ECG中提取出对诊断有意义的信息,并具有并行处理、自学习等特点。     

Data management plans: the missing perspective

The National Institutes of Health requires data sharing plans for projects with over five hundred thousand dollars in direct costs in a single year and has recently released a new guidance on rigor and reproducibility in grant applications. The National Science Foundation outright requires Data Management Plans (DMPs) as part of applications for funding. However, there is no general and definitive list of topics that should be covered in a DMP for a research project. We identified and reviewed DMP requirements from research funders. Forty-three DMP topics were identified. The review uncovered inconsistent requirements for written DMPs as well as high variability in required or suggested DMP topics among funder requirements. DMP requirements were found to emphasize post-publication data sharing rather than upstream activities that impact data quality, provide traceability or support reproducibility. With the emphasis equalized, the forty-three identified topics can aid Data Managers in systematically generating comprehensive DMPs that support research project planning and funding application evaluation as well as data management conduct and post-publication data sharing.     

cDNA基因芯片数据分析软件的研发进展

数据分析与挖掘是基因芯片研究的关键和难点,而软件是数据分析方法实现的主要手段。我们从以下几个方面对cDNA基因芯片分析软件进行了综述。首先介绍了芯片数据的获取及分析的软件,然后概述了不同统计软件在芯片数据分析中的应用,并详细介绍几种常用的芯片数据分析软件,最后简述了基因网络分析及数据挖掘方面的软件。     

Distributed clinical data sharing via dynamic access-control policy transformation

BackgroundData sharing in electronic health record (EHR) systems is important for improving the quality of healthcare delivery. Data sharing, however, has raised some security and privacy concerns because healthcare data could be potentially accessible by a variety of users, which could lead to privacy exposure of patients. Without addressing this issue, large-scale adoption and sharing of EHR data are impractical. The traditional solution to the problem is via encryption. Although encryption can be applied to access control, it is not applicable for complex EHR systems that require multiple domains (e.g. public and private clouds) with various access requirements.ObjectivesThis study was carried out to address the security and privacy issues of EHR data sharing with our novel access-control mechanism, which captures the scenario of the hybrid clouds and need of access-control policy transformation, to provide secure and privacy-preserving data sharing among different healthcare enterprises.MethodsWe introduce an access-control mechanism with some cryptographic building blocks and present a novel approach for secure EHR data sharing and access-control policy transformation in EHR systems for hybrid clouds.ResultsWe propose a useful data sharing system for healthcare providers to handle various EHR users who have various access privileges in different cloud environments. A systematic study has been conducted on data sharing in EHR systems to provide a solution to the security and privacy issues.ConclusionsIn conclusion, we introduce an access-control method for privacy protection of EHRs and EHR policy transformation that allows an EHR access-control policy to be transformed from a private cloud to a public cloud. This method has never been studied previously in the literature. Furthermore, we provide a protocol to demonstrate policy transformation as an application scenario.     

Data Pseudonymization in a Range That Does Not Affect Data Quality: Correlation with the Degree of Participation of Clinicians

Personal medical information is an essential resource for research; however, there are laws that regulate its use, and it typically has to be pseudonymized or anonymized. When data are anonymized, the quantity and quality of extractable information decrease significantly. From the perspective of a clinical researcher, a method of achieving pseudonymized data without degrading data quality while also preventing data loss is proposed herein. As the level of pseudonymization varies according to the research purpose, the pseudonymization method applied should be carefully chosen. Therefore, the active participation of clinicians is crucial to transform the data according to the research purpose. This can contribute to data security by simply transforming the data through secondary data processing. Case studies demonstrated that, compared with the initial baseline data, there was a clinically significant difference in the number of datapoints added with the participation of a clinician (from 267,979 to 280,127 points, P < 0.001). Thus, depending on the degree of clinician participation, data anonymization may not affect data quality and quantity, and proper data quality management along with data security are emphasized. Although the pseudonymization level and clinical use of data have a trade-off relationship, it is possible to create pseudonymized data while maintaining the data quality required for a given research purpose. Therefore, rather than relying solely on security guidelines, the active participation of clinicians is important.     

医学数据挖掘的技术、方法及应用

医学数据挖掘是提高医院信息管理水平，为疾病的诊断和治疗提供科学的、准确的决策，促进远程医疗和社区医疗发展的需要。本文对医学数据挖掘的关键技术——数据的预处理、多属性信息的融合、挖掘算法的高效性与鲁棒性、提供知识的准确性与可靠性等进行了论述；阐述了基于计算智能的医学数据挖掘方法，介绍了人工神经网络、模糊逻辑、遗传算法、粗糙集理论和支持向量机在医学数据挖掘中的应用；最后对医学数据挖掘的特点和亟待解决的问题进行了总结。     

Data Analysis will not Result in Knowledge Production about Sepsis

How to cite this article: Sampath S. Data Analysis will not Result in Knowledge Production about Sepsis. Indian J Crit Care Med 2021;25(7):750–751.     

数字图书馆读者权利与读者服务

通过对数字图书馆读者权利的阐述，目的是使读者维护自身的合法权益不受侵害的同时，更好地利用图书馆，同时对馆员在维护读者权益中所要尽的义务做一探讨。