共查询到19条相似文献,搜索用时 78 毫秒
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数据挖掘(Data Mining,DM)是一门应用性很强的技术。本文首先介绍了与现有的几种重要的DM任务相关的应用问题及实现方法,然后介绍了SPSS公司提出的DM 5A评价标准,以及DM应用中存在的一些问题。最后提出,随着理论的不断深入,工具的不断完善,DM必将成为一门普及技术。 相似文献
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本文简要介绍了我院HIS的应用现状,并对数据仓库和数据挖掘的基础理论进行了简单介绍,最后介绍了数据挖掘在HIS中的简单应用. 相似文献
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目的 探讨饮食治疗对糖尿病(DM)患者控制血糖的重要性.方法 对我院230例DM患者进行饮食指导.结果 通过饮食指导,使DM患者能够主动地配合,自我照顾和管理好饮食,提高生活质量和生命率.结论 通过合理的饮食指导,有效地控制了血糖,减少了并发症的发生,提高了DM患者的生存质量. 相似文献
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医学数据挖掘的技术、方法及应用 总被引:38,自引:0,他引:38
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。 相似文献
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2型糖尿病患者胰岛素抵抗与脂联素、瘦素关系的探讨 总被引:2,自引:0,他引:2
目的:通过对2型糖尿病患者血清脂联素、瘦素水平检测及二者之间的比值分析,探讨DM2胰岛素抵抗发生的机理,为临床诊断治疗提供依据。方法:用酶联免疫分析和放射免疫分析对87例DM2,25例DM1患者和30例正常对照者及52例DM2经一年治疗前后脂联素、瘦素水平进行检测分析。结果:DM2患者脂联素水平明显低于DM1和正常对照组(P<0.01);瘦素水平DM1、DM2均高于正常对照组,DM2明显高于DM1(P<0.01);脂联素/瘦素的比值DM1>1(1.183),DM2<1(0.211),DM1与DM2比值比较有显著性差异(P<0.001);52例DM2治疗前后比较,脂联素、瘦素及二者比值有显著差异(P<0.01),脂联素与瘦素比较呈负相关。结论:2型糖尿病胰岛素抵抗的发生与脂联素、瘦素密切相关,二者的比值变化对糖尿病分型具有重要意义。 相似文献
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目的 了解糖尿病(DM)代谢异常分布情况,为DM防治提供科学依据.方法 对60例DM患者、50例健康对照者进行一般情况调查、体格检查以及实验室检查等.采用SPSS 13.0统计软件包进行统计分析.结果 DM组腰围、超重率、肥胖率明显高于NC组(均P<0.05).DM组的TG、FPG水平明显高于NC组,HDL-C明显低于NC组(均P<0.0001),SBP、DBP水平明显高于NC组(均P<0.05).结论 DM患者在血糖、血脂异常的同时还常伴有高血压以及肥胖等危险因素存在,对这些危险因素必须加以监测和控制,以减少DM心血管并发症的发病危险. 相似文献
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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。 相似文献
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Zhiping Wang Seongho Kim Sara K. Quinney Yingying Guo Stephen D. Hall Luis M. Rocha Lang Li 《Journal of biomedical informatics》2009,42(4):726-735
A feasibility study of literature mining is conducted on drug PK parameter numerical data with a sequential mining strategy. Firstly, an entity template library is built to retrieve pharmacokinetics relevant articles. Then a set of tagging and extraction rules are applied to retrieve PK data from the article abstracts. To estimate the PK parameter population-average mean and between-study variance, a linear mixed meta-analysis model and an E–M algorithm are developed to describe the probability distributions of PK parameters. Finally, a cross-validation procedure is developed to ascertain false-positive mining results. Using this approach to mine midazolam (MDZ) PK data, an 88% precision rate and 92% recall rate are achieved, with an F-score = 90%. It greatly out-performs a conventional data mining approach (support vector machine), which has an F-score of 68.1%. Further investigate on 7 more drugs reveals comparable performances of our sequential mining approach. 相似文献
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通过对目前医学数据资料状况的分析,提出了数据挖掘与医学数据资源开发利用的方法。文章首先介绍了聚类分析、关联分析、决策树三个数据挖掘方法及实例。实例资料结果说明通过分析疾病的分类、疾病与药物之间的关联,可以为疾病的早期预测,为相关疾病的诊疗路径提供帮助。因此聚类分析、关联分析以及决策树是医学数据挖掘中的主要方法,三者的结合可以对某一类疾病进行系统的分析。 相似文献
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Health care information systems tend to capture data for nursing tasks, and have little basis in nursing knowledge. Opportunity lies in an important issue where the knowledge used by expert nurses (nursing knowledge workers) in caring for patients is undervalued in the health care system. The complexity of nursing's knowledge base remains poorly articulated and inadequately represented in contemporary information systems. There is opportunity for data mining methods to assist with discovering important linkages between clinical data, nursing interventions, and patient outcomes. Following a brief overview of relevant data mining techniques, a preterm risk prediction case study illustrates the opportunities and describes typical data mining issues in the nontrivial task of building knowledge. Building knowledge in nursing, using data mining or any other method, will make progress only if important data that capture expert nurses' contributions are available in clinical information systems configurations. 相似文献
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Nattkemper TW 《Journal of biomedical informatics》2004,37(5):380-391
In recent years, multivariate imaging techniques are developed and applied in biomedical research in an increasing degree. In research projects and in clinical studies as well m-dimensional multivariate images (MVI) are recorded and stored to databases for a subsequent analysis. The complexity of the m-dimensional data and the growing number of high throughput applications call for new strategies for the application of image processing and data mining to support the direct interactive analysis by human experts. This article provides an overview of proposed approaches for MVI analysis in biomedicine. After summarizing the biomedical MVI techniques the two level framework for MVI analysis is illustrated. Following this framework, the state-of-the-art solutions from the fields of image processing and data mining are reviewed and discussed. Motivations for MVI data mining in biology and medicine are characterized, followed by an overview of graphical and auditory approaches for interactive data exploration. The paper concludes with summarizing open problems in MVI analysis and remarks upon the future development of biomedical MVI analysis. 相似文献
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知识发现及其在临床医学上的应用 总被引:2,自引:0,他引:2
目前海量的数据存储已经远远超过了人的理解能力 ,尤其是现代医学数据量越来越大 ,而传统的统计技术及数据管理工具已经力不从心 ,知识发现及数据挖掘工具在处理海量数据库时显示了它们的长处。本文首先介绍了知识发现及数据挖掘的概念 ,又详细介绍了知识发现的一个重要工具粗糙集理论的有关基础知识并举例介绍了其应用。 相似文献
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《Journal of biomedical informatics》2013,46(5):849-856
The Gene Ontology (GO), a set of three sub-ontologies, is one of the most popular bio-ontologies used for describing gene product characteristics. GO annotation data containing terms from multiple sub-ontologies and at different levels in the ontologies is an important source of implicit relationships between terms from the three sub-ontologies. Data mining techniques such as association rule mining that are tailored to mine from multiple ontologies at multiple levels of abstraction are required for effective knowledge discovery from GO annotation data. We present a data mining approach, Multi-ontology data mining at All Levels (MOAL) that uses the structure and relationships of the GO to mine multi-ontology multi-level association rules. We introduce two interestingness measures: Multi-ontology Support (MOSupport) and Multi-ontology Confidence (MOConfidence) customized to evaluate multi-ontology multi-level association rules. We also describe a variety of post-processing strategies for pruning uninteresting rules. We use publicly available GO annotation data to demonstrate our methods with respect to two applications (1) the discovery of co-annotation suggestions and (2) the discovery of new cross-ontology relationships. 相似文献
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传统搜索引擎的搜索结果采用的是以一维列表的形式展现,随着文献数据的急剧增多,用户对于搜索结果的辨识和分析速度也在急剧下降.为了弥补传统工具的这一不足,本文开发了"语义图"(semantic map,SMAP),此工具对文献数据进行数据挖掘和可视化,包括关联匹配和聚类,将搜索结果以二维矩阵图的形式展示出来,方便用户理解数据之间的内部联系,并帮助用户迅速从整体上把握搜索结果.最后以蛋白质组学文献分析过程为例具体展示了此工具的应用. 相似文献