数据挖掘在医疗项目成本核算中的应用

目的:提高医疗项目成本核算的准确度和时效性。方法:打破数据结构限制,拓宽项目成本数据来源;分析数据之间的关联,提取隐含成本信息;采用全体而非样本数据参与计算,减少核算误差。结果:数据挖掘的应用能有效解决目前医疗项目成本核算中碰到的瓶颈问题。结论:医院应建设统一的数据平台,普及业务层面的信息化管理,加强专业人才培养,促进数据挖掘技术在医疗项目成本核算中的应用。     

基于数据挖掘的艾滋病综合防治居民健康档案管理信息系统设计与实现

目的 提高居民艾滋病综合防治水平,动态监测居民艾滋病防治信息,实现与其他信息管理系统的互联互通、资源共享,方便数据挖掘.方法 根据《国家基本公共卫生服务规范》及《艾滋病疫情信息报告管理规范》等文件规定要求,结合艾滋病防治工作,采用B/S结构,SSH框架,ORACLE10.0数据库,基于数据挖掘技术建立数据库,开发居民健康档案信息管理系统.结果 基于数据挖掘技术,开发了居民健康档案信息管理系统.结论 建立艾滋病综合防治新型电子健康档案,完成4个项目县2 365 680人基本信息数据资料的建档工作,除对数据进行基本统计分析外,将数据导出后采用决策树、关联规则、神经网络等挖掘技术对数据进行分析,取得了较好效果.将信息网络覆盖到4个项目地区的卫生行政部门及县、乡、村3级艾滋病防治机构,并使系统正常运行,实现了对居民艾滋病防治信息健康的动态管理.     

ENCODE项目科学数据出版模式创新

【目的】以ENCODE项目为例,探讨数字环境下科学数据的出版模式。【方法】从Nature ENCODE Explorer、Thread（线程）和ENCODE analysis virtualmachine三个方面分析ENCODE项目的出版模式创新方案,并介绍引导工具的呈现效果,展望其后续发展状况。【结果】ENCODE项目基于可视化技术和数据挖掘技术,创造易于使用的可视化工具,对项目成果进行有效整合;引入分析工具帮助用户进行数据集重用和计算。【结论】数字环境下应该重视整合不同出版平台的数据资源加以分析利用,探索实现科学数据开放和共享的有效方法。     

基于EMR数据挖掘的临床路径构建中EMR数据预处理

高质量决策取决于高质量数据.与其他领域的数据挖掘一样,在基于电子病历数据挖掘构建临床路径中,也需要对拟挖掘的电子病历数据进行预处理,为最终的数据挖掘提供干净、准确、更有针对性的数据,从而提高挖掘的效率和准确度,但对于基于EMR数据挖掘的临床数据构建中的电子病历预处理又有其自身的特点.文章对此进行了分析与探索.     

基于大数据的卫生经济管理数据挖掘

描述了“大数据”对卫生经济管理发展的作用以及卫生经济管理数据挖掘整合的现状,指出要关注数据标准的统一管理、信息系统的整合管理、医疗相关数据的采集、数据挖掘整合流程的规范等卫生经济管理数据挖掘的核心要素,转变理念,储备技术,建立制度,为卫生经济宏观决策、预算分配、执行分析、绩效考核等方面提供强有力的技术支撑.     

数据挖掘在口岸卫生检验检疫中的应用分析

数据挖掘是利用各种分析工具在海量数据中发现模型和数据间关系的过程。本文阐述了数据的主要任务、方法、步骤和挖掘工具,对在口岸卫生检验检疫管理中的数据挖掘应用进行了研究和探讨。     

HIS中的数据挖掘

随着医院信息系统在医院的广泛应用,海量的数据的管理以及对这些数据的深入分析处理需要先进的数据存储和管理技术,数据挖掘在HIS中的应用具有广阔前景。本文首先阐述了数据仓库及数据挖掘的基本思想,在此基础上着重探讨了数据挖掘技术在HIS的应用。     

数据挖掘在生命科学中的应用

从海量数据中提取知识的新技术,即数据挖掘,是一个利用各种分析工具在海量数据中发现模型和数据间关系的过程,这些模型和关系可以用来做出预测。介绍了数据挖掘的任务、方法、基本过程、在生命科学中的应用、研究趋势和发展方向。     

自组织映射模型在医学领域数据挖掘中的应用

目的 介绍自组织映射模型的基本原理、常见算法及其研究进展,阐述自组织映射模型在医学领域数据挖掘中的应用,以期为相关研究提供参考方法 本文从医学领域的数据挖掘角度出发,采用文献回顾的方法,阐述自组织映射模型在处理非线性、高度冗余数据时运算速度和精度上的优势,并分析自组织映射模型各种算法的特点及不足结果 相对于传统的数据挖掘方法在大规模的医学数据挖掘时无法消除数据冗余及捕捉非线性特征,从而导致挖掘过程速度慢、精度低等问题,自组织映射模型可以自动地、智能地、可视化地将待处理的数据转化为有用的知识,较好地弥补传统数据挖掘方法的不足.自组织映射模型算法的不断改进,保证了它可以适用于具有不同特征的数据结论 自组织映射模型在医学领域的数据挖掘中得到了较好的应用,其算法还有进一步改进的空间,在医学领域的应用前景将会得到进一步扩展.     

军卫一号系统中的病人数据挖掘

本文基于数字化医院积累的大量临床信息,结合计算机领域内的数据挖掘技术,提出一套病人数据挖掘的概念和方法。通过对大量临床信息进行数据挖掘,不仅能为临床诊断提供有用信息,而且可以从大量的数据中发现隐藏在数据中的趋势、规律和异常。这些信息对临床诊断、科研、教学等都具有非常重要的参考价值。     

Prioritization of malaria endemic zones using self-organizing maps in the Manipur state of India

Due to the availability of a huge amount of epidemiological and public health data that require analysis and interpretation by using appropriate mathematical tools to support the existing method to control the mosquito and mosquito-borne diseases in a more effective way, data-mining tools are used to make sense from the chaos. Using data-mining tools, one can develop predictive models, patterns, association rules, and clusters of diseases, which can help the decision-makers in controlling the diseases. This paper mainly focuses on the applications of data-mining tools that have been used for the first time to prioritize the malaria endemic regions in Manipur state by using Self Organizing Maps (SOM). The SOM results (in two-dimensional images called Kohonen maps) clearly show the visual classification of malaria endemic zones into high, medium and low in the different districts of Manipur, and will be discussed in the paper.     

主成分分析在经前期综合症数据处理中的应用

"经前期综合症(PMS)肝气逆证辨证规范与疗效评价标准研究"项目收集了较大数量的数据,应用主成分分析进行降维处理,提取出经前期综合症的主证候要素并探讨证候要素的分布,运用SQLAS(Analysis Services)实现聚集检验,初步构建经前期综合症的辩证及数学模型。     

传染病监测关口前移背景下医院数据的挖掘方法

基于医院信息系统平台构建的医院传染病疫情报告系统在传染病监测和防控体系中发挥着越来越重要的作用,医院信息系统数据库包括电子病例的文本数据库、时间数据库和时间序列数据库亦在日趋完善。本研究从医院信息系统中的数据出发,简要介绍基于医院信息的数据挖掘常用方法。     

Data mining and computationally intensive methods: summary of Group 7 contributions to Genetic Analysis Workshop 13

The Framingham Heart Study data, as well as a related simulated data set, were generously provided to the participants of the Genetic Analysis Workshop 13 in order that newly developed and emerging statistical methodologies could be tested on that well-characterized data set. The impetus driving the development of novel methods is to elucidate the contributions of genes, environment, and interactions between and among them, as well as to allow comparison between and validation of methods. The seven papers that comprise this group used data-mining methodologies (tree-based methods, neural networks, discriminant analysis, and Bayesian variable selection) in an attempt to identify the underlying genetics of cardiovascular disease and related traits in the presence of environmental and genetic covariates. Data-mining strategies are gaining popularity because they are extremely flexible and may have greater efficiency and potential in identifying the factors involved in complex disorders. While the methods grouped together here constitute a diverse collection, some papers asked similar questions with very different methods, while others used the same underlying methodology to ask very different questions. This paper briefly describes the data-mining methodologies applied to the Genetic Analysis Workshop 13 data sets and the results of those investigations.     

SARS诊疗信息展示与描述

目的：探讨完整全面的SARS诊疗信息储存与展示方法，为相关研究构建数据利用平台。方法：根据HIS中已有信息，分析对诊断治疗有参考价值的所有信息，结合SARS疾病特点增补部分内容，利用数据库技术与统计描述手段，完整全面提取数据。结果：完成了SARS诊疗信息储存与展示的软件工具。结论：完整的原始资料，辅以适当的统计图形，为各研究进行数据挖掘提供可靠支持。     

Telematic integration of health data: a practicable contribution

The patients' clinical and healthcare data should virtually be available everywhere, both to provide a more efficient and effective medical approach to their pathologies, as well as to make public healthcare decision makers able to verify the efficacy and efficiency of the adopted healthcare processes. Unfortunately, customised solutions adopted by many local Health Information Systems in Italy make it difficult to share the stored data outside their own environment. In the last years, worldwide initiatives have aimed to overcome such sharing limitation. An important issue during the passage towards standardised, integrated information systems is the possible loss of previously collected data. The herein presented project realises a suitable architecture able to guarantee reliable, automatic, user-transparent storing and retrieval of information from both modern and legacy systems. The technical and management solutions provided by the project avoid data loss and overlapping, and allow data integration and organisation suitable for data-mining and data-warehousing analysis.     

Following in the footnotes of giants: citation analysis and its discontents

Reflecting on his own personal history with bibliometrics, the author places it in the broader context of research with available information and data-mining. In so doing, he considers the utility of bibliometrics for raising new questions and its limitations for guiding decision-making.     

Migraineurs were reliably identified using administrative data

BackgroundMigraine is a common and important source of pain and disability in society. Accurately identifying such people using routinely collected health data would be beneficial for health services research.ObjectiveExternally validate a previously published method to identify migraineurs using health administrative data; and determine if a better model can be derived using data-mining techniques.MethodsMigraine status was determined for Ontarians participating in a population-based, cross-sectional survey. Consenting participants were linked to population-based health administrative data to identify age, sex, and coded diagnoses. Discrimination and calibration measures were used to appraise the models. A de novo technique we term “double threshold analysis” was used to determine optimal lower and upper expected probabilities to identify migraine status in the newly derived model.ResultsA total of 1,01,114 people (mean age 46 years, 46% male) were included in the study, of which 11,314 (11.2%) had migraines. Using data-driven parameter estimates, the previous model to identify migraineurs had adequate discrimination (c-statistic 0.707 [95% CI 0.701–0.712]) and calibration (Hosmer–Lemeshow [H–L] statistic 20.8). A new model that included diagnostic code scores for physician visits, emergency visits, and hospitalizations with nonlinear terms for age and interactions significantly improved the model (c-statistic 0.724 [0.716–0.733], 16.4). Categorizing all people with a predicted migraine probability less than 10% or greater than 90% as without and having the disease, respectively, resulted in a sensitivity of 3.1%, a specificity of 99.96%, and a positive predictive value of 81.0% while capturing 57.0% of the cohort and 29.3% of migraineurs.ConclusionA previously derived model to identify migraineurs was improved using data-mining techniques permitting accurate cohort identification using routinely collected health administrative data.