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2008 21st IEEE International Symposium on Computer-Based Medical Systems最新文献

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Supporting Practices of Positive Redundancy for Seamless Care 为无缝护理提供积极冗余的支持实践
Pub Date : 2008-06-17 DOI: 10.1109/CBMS.2008.26
F. Cabitza, C. Simone
The paper shows how redundancy can be put at work to play a positive role in facilitating the cognitive and coordinative tasks of clinicians in a ward setting. The main requirement to accomplish this positive function is to allow and support clinicians in annotating the clinical record and making correlations between redundant data explicit. We report an observational study we undertook in the design of coordination mechanisms based on a minimal set of meaningful correlations.
论文显示了如何冗余可以放在工作中发挥积极作用,促进在病房设置临床医生的认知和协调任务。实现这一积极功能的主要要求是允许和支持临床医生注释临床记录,并明确冗余数据之间的相关性。我们报告了一项观察性研究,我们在设计协调机制的基础上进行了一组最小的有意义的相关性。
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引用次数: 13
Texture Based Classification and Segmentation of Tissues Using DT-CWT Feature Extraction Methods 基于纹理的组织分类与分割的DT-CWT特征提取方法
Pub Date : 2008-06-17 DOI: 10.1109/CBMS.2008.46
D. Aydogan, M. Hannula, T. Arola, P. Dastidar, J. Hyttinen
In this study, four different dual-tree complex wavelet (DT-CWT) based texture feature extraction methods are developed and compared to segment and classify tissues. Methods that are proposed in this study are based on local energy calculations of sub-bands. Two of the methods use rotation variant texture features and the other two use rotation invariant features. The methods are tested on two texture compositions from the Brodatz texture database and two actual magnetic resonance (MR) images. Results show that there is not a significant difference between using rotation variant or invariant features. On the other hand, for the same Brodatz textures, all DT-CWT based feature extraction methods are competitive with other filtering approaches.
本研究开发了四种基于双树复小波(DT-CWT)的纹理特征提取方法,并对其进行了分割和分类。本研究提出的方法是基于子波段的局部能量计算。其中两种方法使用旋转可变纹理特征,另外两种方法使用旋转不变特征。在Brodatz纹理数据库中的两个纹理组合和两个实际的磁共振图像上对这些方法进行了测试。结果表明,旋转变特征和旋转不变特征的使用没有显著差异。另一方面,对于相同的Brodatz纹理,所有基于DT-CWT的特征提取方法都与其他滤波方法竞争。
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引用次数: 12
A Two-Step Approach for Feature Selection and Classifier Ensemble Construction in Computer-Aided Diagnosis 计算机辅助诊断中特征选择与分类器集成两步法
Pub Date : 2008-06-17 DOI: 10.1109/CBMS.2008.68
Michael C. Lee, L. Böröczky, Kivilcim Sungur-Stasik, Aaron D. Cann, A. Borczuk, S. Kawut, C. Powell
Accurate classification methods are critical in computer-aided diagnosis and other clinical decision support systems. Previous research has studied methods for combining genetic algorithms for feature selection with ensemble classifier systems in an effort to increase classification accuracy. We propose a two-step approach that first uses genetic algorithms to reduce the number of features used to characterize the data, then applies the random subspace method on the remaining features to create a set of diverse but high performing classifiers. These classifiers are combined using ensemble learning techniques to yield a final classification. We demonstrate this approach for computer-aided diagnosis of solitary pulmonary nodules from CT scans, in which the proposed method outperforms several previously described methods.
准确的分类方法对计算机辅助诊断和其他临床决策支持系统至关重要。先前的研究已经研究了将遗传算法与集成分类器系统相结合的方法来提高分类精度。我们提出了一种两步方法,首先使用遗传算法减少用于表征数据的特征的数量,然后在剩余的特征上应用随机子空间方法来创建一组多样化但高性能的分类器。这些分类器结合使用集成学习技术来产生最终的分类。我们证明了这种方法用于计算机辅助诊断CT扫描中的孤立性肺结节,其中所提出的方法优于先前描述的几种方法。
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引用次数: 20
Easing the Formalization of Clinical Guidelines with a User-tailored, Extensible Agile Model Driven Development (AMDD) 使用用户定制的可扩展敏捷模型驱动开发(AMDD)简化临床指南的形式化
Pub Date : 2008-06-17 DOI: 10.1109/CBMS.2008.92
P. Martini, K. Kaiser, S. Miksch
Transforming a text-based clinical guideline in a computer-interpretable form is a time-consuming and demanding task due to the various users involved, who have different technical and medical background. In the past, different guideline representation languages and supporting tools have been developed, however, these approaches seldom address the various users' demands and needs in the different steps of the guidelines' life cycle. Our approach is oriented on the guideline life-cycle and takes the requirements and the interactions of the various actors into account to formalize guidelines in a computer-interpretable guideline representation by using a semi-automatic way based on NLP techniques. We analyzed the guideline life-cycle and the roles of the actors to build such a model. This model is prototypical implemented and showed the usefulness and utility for the various users.
将基于文本的临床指南转换为计算机可解释的形式是一项耗时且艰巨的任务,因为涉及的用户不同,他们具有不同的技术和医学背景。过去,虽然开发了不同的指南表示语言和支持工具,但这些方法很少能够满足指南生命周期不同阶段用户的不同需求。我们的方法以指南生命周期为导向,并考虑到各种参与者的需求和相互作用,通过使用基于NLP技术的半自动方式将指南形式化为计算机可解释的指南表示。我们分析了指南的生命周期和参与者的角色来构建这样的模型。该模型是原型实现的,并显示了对各种用户的有用性和实用性。
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引用次数: 2
@neurIST - Towards a System Architecture for Advanced Disease Management through Integration of Heterogeneous Data, Computing, and Complex Processing Services 通过异构数据、计算和复杂处理服务的集成实现高级疾病管理的系统架构
Pub Date : 2008-06-17 DOI: 10.1109/CBMS.2008.42
H. Rajasekaran, Luigi Lo Iacono, P. Hasselmeyer, J. Fingberg, P. Summers, S. Benkner, G. Engelbrecht, A. Arbona, A. Chiarini, C. Friedrich, M. Hofmann-Apitius, K. Kumpf, Bob Moore, P. Bijlenga, J. Iavindrasana, Henning Müller, R. Hose, R. Dunlop, Alejandro F Frangi
This paper presents the system architecture of the @neurIST project, which aims at supporting the research and treatment of cerebral aneurysms by bringing together heterogeneous data, computing and complex processing services. The architecture is generic enough to adapt it to the treatment of other diseases beyond cerebral aneurysms. The paper describes the generic requirements of the system and presents the architecture, applications and middleware technologies used to realise the system and highlights the innovations in @neurIST.
本文介绍了@neurIST项目的系统架构,该项目旨在通过汇集异构数据、计算和复杂处理服务来支持脑动脉瘤的研究和治疗。这种结构足够通用,可以用于治疗脑动脉瘤以外的其他疾病。本文描述了该系统的一般需求,介绍了实现该系统的体系结构、应用程序和中间件技术,并重点介绍了@neurIST的创新之处。
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引用次数: 27
Web-Based System for Advanced Heart Disease Identification Using Grid Computing Technology 基于web的基于网格计算技术的高级心脏病识别系统
Pub Date : 2008-06-17 DOI: 10.1109/CBMS.2008.106
Changhee Han, Chan-Hyun Youn, W. Jung
Heart disease is one of the most serious medical problems which threaten human life. Now there are several methods for detecting heart disease and ECG (electrocardiogram) signal analysis is one of the typical solutions. The other method such as MCG (magnetocardiogram) is also recommended for heart disease detection. In computer society, the e-Health systems using these diagnosis methods have been developed. However, each diagnosis method has their own weak points and also limitations in system performance according to the increase of data in quantity. Therefore, in order to improve each diagnosis method and conventional e-Health system, we propose and implement a novel integrated-diagnosis system combining grid technologies which is termed as a Physio-Grid system. We present the experimental and evaluation data in order to show our system capability in diagnosis and system performance. The results indicate that the proposed e-Health system can provide the plausible medical services and also it guarantees high reliability and system performance in data management and in diagnosis.
心脏病是威胁人类生命的最严重的医学问题之一。目前有几种检测心脏病的方法,心电图信号分析是其中一种典型的解决方案。另一种方法如MCG(心脏磁图)也被推荐用于心脏病检测。在计算机社会中,利用这些诊断方法的电子卫生系统得到了发展。然而,随着数据量的增加,每种诊断方法都有其自身的弱点和系统性能的局限性。因此,为了改进各种诊断方法和传统的电子医疗系统,我们提出并实现了一种结合网格技术的新型综合诊断系统,称为物理网格系统。为了展示系统的诊断能力和系统性能,我们给出了实验和评估数据。结果表明,所提出的电子医疗系统能够提供合理的医疗服务,在数据管理和诊断方面保证了较高的可靠性和系统性能。
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引用次数: 8
Workflow Integration in VL-e Medical VL-e Medical中的工作流集成
Pub Date : 2008-06-17 DOI: 10.1109/CBMS.2008.114
T. Glatard, Kamel Boulebiar, S. Olabarriaga
This paper presents the integration of a workflow management system into the VL-e medical software architecture. Workflows are designed with the Taverna workbench, and then executed with the MOTEUR engine on the EGEE grid through the VBrowser, which is the basic front-end for grid-enabled applications in the VL-e medical project. Data management is handled by the virtual file system of the VL-e toolkit. The resulting system provides a high-level interface to execute grid applications.
本文介绍了工作流管理系统与VL-e医疗软件体系结构的集成。工作流是用Taverna工作台设计的,然后通过VBrowser在EGEE网格上使用MOTEUR引擎执行,这是VL-e医疗项目中支持网格的应用程序的基本前端。数据管理由VL-e工具包的虚拟文件系统处理。生成的系统提供了执行网格应用程序的高级接口。
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引用次数: 8
Public Yet Private: The Status, Durability and Visibility of Handover Sheets 公共与私有:交接单的地位、持久性和可见性
Pub Date : 2008-06-17 DOI: 10.1109/CBMS.2008.52
R. Randell, P. Woodward, Stephanie M. Wilson, J. Galliers
Drawing on data from a multi-site case study of a range of clinical settings, this paper explores the form of nursing handover sheets and the processes through which they are created and updated. We argue that these documents function as both public and private documents, having relevance for the whole ward while also acting as a personal workspace. Such dual functionality needs to be supported by any technology that seeks to provide for the work of handover, if the handover sheet is to continue to act as a space for work, rather than just a repository of information.
根据一系列临床设置的多地点案例研究的数据,本文探讨了护理交接表的形式及其创建和更新的过程。我们认为这些文件既可以作为公共文件,也可以作为私人文件,与整个病房相关,同时也可以作为个人工作区。如果要让交接表继续充当工作空间,而不仅仅是信息存储库,那么任何寻求提供交接工作的技术都需要支持这种双重功能。
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引用次数: 13
An Integrated Data Mining System for Patient Monitoring with Applications on Asthma Care 一种集成数据挖掘系统,用于哮喘护理患者监测
Pub Date : 2008-06-17 DOI: 10.1109/CBMS.2008.111
V. Tseng, Chao-Hui Lee, Jessie Chia-Yu Chen
In this paper, we proposed an integrated data mining system for patient monitoring with applications on asthma care. In this system, two data mining methods named PBD and PBC are designed for predicting asthma attacks. The main methodology is to extract the significant information of asthma attacks and build classifiers by using users' daily bio-signal records and environmental data. Meanwhile, helpful medical information and suggestions supported by doctors are applied. In this way, the proposed system can predict the chances of asthma attacks and provide patients with the proper medical instructions or health messages. The experimental evaluation results proved that the proposed mechanism is effective and reliable in asthma attack prediction.
在本文中,我们提出了一个集成的数据挖掘系统,用于患者监测和哮喘护理的应用。在该系统中,设计了PBD和PBC两种数据挖掘方法来预测哮喘发作。主要方法是利用用户的日常生物信号记录和环境数据提取哮喘发作的重要信息并构建分类器。同时,应用有益的医学信息和医生支持的建议。通过这种方式,提出的系统可以预测哮喘发作的几率,并为患者提供适当的医疗指导或健康信息。实验评价结果证明了该机制对哮喘发作预测的有效性和可靠性。
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引用次数: 15
A Novel Approach to Extract Structured Motifs by Multi-Objective Genetic Algorithm 一种基于多目标遗传算法的结构基序提取方法
Pub Date : 2008-06-17 DOI: 10.1109/CBMS.2008.99
Mehmet Kaya, Melikali Güç
The functional motifs composed of several sequential blocks are difficult to find. Current mining methods might individually find each motif block but fail to connect them with large irregular gaps. In this paper we propose a novel method for the efficient extraction of structured motifs from DNA sequences using multi-objective genetic algorithm. The main advantage of our approach is that a large number of nondominated motifs can be obtained by a single run with respect to conflicting objectives: similarity and support maximization and gap minimization. To the best of our knowledge, this is the first effort in this direction. The proposed method can be applied to any data set with a sequential character. Furthermore, it allows any choice of similarity measures for finding motifs. By analyzing the obtained optimal motifs, the decision maker can understand the tradeoff between the objectives. We compare our method with the two well-known structured motif extraction methods, EXMOTIF and RISOTTO. Experimental results on synthetics data set demonstrate that the proposed method exhibits good performance over the other methods in terms of runtime.
由几个连续块组成的功能基元很难找到。目前的挖掘方法可能会单独找到每个motif块,但无法将它们与大的不规则间隙连接起来。本文提出了一种利用多目标遗传算法从DNA序列中高效提取结构基序的新方法。我们的方法的主要优点是,相对于相互冲突的目标:相似性和支持最大化以及间隙最小化,单次运行可以获得大量非支配的母题。据我们所知,这是在这个方向上的第一次努力。该方法适用于任何具有序列字符的数据集。此外,它允许选择任何相似度量来寻找基序。通过分析得到的最优动机,决策者可以了解目标之间的权衡。我们将我们的方法与两种著名的结构化基序提取方法EXMOTIF和RISOTTO进行了比较。在综合数据集上的实验结果表明,该方法在运行时间上优于其他方法。
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引用次数: 5
期刊
2008 21st IEEE International Symposium on Computer-Based Medical Systems
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