The overall aim of this proposal is to test the clinical effects of a Computer Assisted Decision Support (CADS) System for the management of Type 2 diabetes (T2D) by primary care providers (PCPs). Moreover, the aims are to compare longitudinal patterns of change within and between patients who are managed with the CADS system for differing durations. This comparison will help us to understand the clinical utility of using the CADS system continuously or up to a certain threshold of patient improvement. To achieve these aims, we requested a second year of funding (first year funded through United States Army Medical Research Acquisition Activity USAMRAA), contract number W81XWH-09- 2-0196, for a prospective, cluster, randomized controlled trial (RCT). The ongoing project is a multi-site study including the Walter Reed National military Medical Center, Fort Belvoir Community Hospital (FBCH), and the Kimbrough Ambulatory Care Center. The proposal herein is not duplicative of any current study but rather an extension of the already funded one. A detailed, technical explanation of the software and hardware elements of this study are included in reports for the original CADS study and available upon request.

关键词：计算机应用；决策支持系统；糖尿病

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Incorporating clinical laboratory test results into certified electronic health record (EHR) technology as structured data is a core requirement for eligible hospitals and professionals under Stage 2 of the Medicare and Medicaid EHR Incentive Programs. Currently, there is limited information concerning the readiness of clinical laboratories to deliver structured test results. To address this gap in knowledge, the Office of the National Coordinator for Health IT (ONC) sponsored a national survey of independent and hospital laboratories. This brief describes the capability and extent to which these laboratories send test results as structured data to ordering practitioners EHR systems.

关键词：医疗信息交换；临床医学；实验室；测试结果

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Clinical trials are crucial to determining the safety of medical interventions and their ability to achieve particular health outcomes. Clinical trials are required by regulatory authorities around the world before a new medical product can be brought to market, or before a new indication, formulation, or target population can be approved for an intervention already on the market (ICH, 1995). After a products introduction, additional clinical trials are commonly conducted by industry, government, and academia to further define the relative safety and efficacy (or effectiveness) of the product. Clinical trials are also used to study interventions that do not involve regulated medical products, for example, surgical techniques, behavioral interventions, or studies designed to improve disease management practice (Califf, 2013). Vast amounts of data are generated over the course of a clinical trial. These data are held by the sponsors conducting the clinical trial, and in some instances, by participants or their advocates (Drazen, 2002; Terry and Terry, 2011). Depending on the regulatory jurisdiction, data might or might not be shared or made available to the public for secondary uses. Shared data might include both summary data and individual patient data. In the United States, if a sponsor is seeking regulatory approval, data are shared in confidence with regulators. Select study data might also be made available to individual researchers on a case by case basis upon request, or could be made publicly available, usually at the summary level, for example, through publication in a peer-reviewed journal or through publicly accessible clinical trial registration sites.

关键词：临床试验；数据共享；医疗措施

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The driver-specific data available from naturalistic driving studies provide a unique perspective from which to test and calibrate car-following models. As equipment and data storage costs continue to decline, the collection of data through in situ probe-type vehicles is likely to become more popular, and thus there is a need to assess the feasibility of these data for the modeling of driver car-following behavior. This study focused on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to use naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study was based on data from the database of the 100-Car Study conducted by the Virginia Tech Transportation Institute. One thousand minutes' worth of data comprising more than 2,000 car-following events recorded across eight drivers from a section of multilane highway located near Washington, D.C., was compiled. The collected event data were used to calibrate four different car-following models, and a comparative analysis of model performance was conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed versus time and headway versus time. When compared with the Gipps, intelligent driver, and Gaxis-Herman-Rothery models, the Rakha-Pasumarthy-Adjerid model was found to perform best in matching individual drivers and in matching aggregate results.

关键词：汽车；跟车行为；数据建模

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Shared spaces are being implemented in many countries to deal with safety concerns and traffic flow problems on busy urban streets and street crossings. However, shared space concepts could not be tested before they were built because of the lack of a functioning microscopic shared space simulation. Lane-based car-following models, currently used in traffic simulation, cannot reproduce the high heterogeneity of a mixed traffic mode's nonchannelized flow. This paper's novel approach introduces an extended social force model for vehicles and pedestrians that incorporates social interactions between different modes of transport rather than following a purely rule-based approach. The calibration of such a microscopic traffic simulation model with real-world data from two shared space sites is presented. The simulation can reproduce real-life shared space behavior by comparing it with trajectory and interaction data collected at implemented shared space road designs.

关键词：汽车；仿真系统；模拟比较

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To develop an effective software for finite element (FE) model updating of bridges, the interface technology between VC++ and MATLAB was investigated firstly, and then a software for updating FE model of bridges, named 'Doctor for Bridges' (version 1.0) was developed. Finally, a model ofconcrete-filled steel tube arch bridge was applied to verify the performance and effectiveness of the proposed software.

关键词：桥梁；有限元模型修正；接口技术

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Biomedical research is drowning in data, yet starving for knowledge. Current challenges in biomedical research and clinical practice include information overload - the need to combine vast amounts of structured, semi-structured, weakly structured data and vast amounts of unstructured information - and the need to optimize workflows, processes and guidelines, to increase capacity while reducing costs and improving efficiencies. In this paper we provide a very short overview on interactive and integrative solutions for knowledge discovery and data mining. In particular, we emphasize the benefits of including the end user into the "interactive" knowledge discovery process. We describe some of the most important challenges, including the need to develop and apply novel methods, algorithms and tools for the integration, fusion, pre-processing, mapping, analysis and interpretation of complex biomedical data with the aim to identify testable hypotheses, and build realistic models. The HCI-KDD approach, which is a synergistic combination of methodologies and approaches of two areas, Human-Computer Interaction (HCI) and Knowledge Discovery & Data Mining (KDD), offer ideal conditions towards solving these challenges: with the goal of supporting human intelligence with machine intelligence. There is an urgent need for integrative and interactive machine learning solutions, because no medical doctor or biomedical researcher can keep pace today with the increasingly large and complex data sets - often called "Big Data".

关键词：知识发现；数据挖掘；机器学习；生物医学

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The post-genomic era is producing an enormous volume of data. Efficient applications and protocols are necessary in Bioinformatics to deal with this information. One of the most-challenging goals is the integration of knowledge extracted from different biological databases. Emerging ontologies in Molecular biology are very promising tools to provide sequence annotation standards that can be shared among genome annotation projects. The Gene Ontology (GO) is the most popular vocabulary to assign biological functions to genes, accordingly to evidence obtained from literature or computationally inferred. GO characterization of gene products is now an essential step of each genome annotation pipeline. Genome curators can use, in addition, auxiliar ontologies to describe biological sequences and alignments. The Open Biomedical Ontologies (OBO) consortium, a joint effort of bioinformatics and biomedical communities, has recently defined a common framework to standardize the ontologies developed in different fields. This chapter provides a comprehensive description of GO and other similar ontologies to annotate genome products.

关键词：后基因组时代；元数据；生物信息学；生物医学

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Text is a very important type of data within the biomedical domain. For example, patient records contain large amounts of text which has been entered in a non-standardized format, consequently posing a lot of challenges to processing of such data. For the clinical doctor the written text in the medical findings is still the basis for decision making -neither images nor multimedia data. However, the steadily increasing volumes of unstructured information need machine learning approaches for data mining, i.e. text mining. This paper provides a short, concise overview of some selected text mining methods, focusing on statistical methods, i.e. Latent Semantic Analysis, Probabilistic Latent Semantic Analysis, Latent Dirichlet Allocation, Hierarchical Latent Dirichlet Allocation, Principal Component Analysis, and Support Vector Machines, along with some examples from the biomedical domain. Finally, we provide some open problems and future challenges, particularly from the clinical domain, that we expect to stimulate future research.

关键词：文本挖掘；自然语言处理；非结构化信息；生物医学

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Life can be regarded as a complex system in which genes, gene products, and other metabolites interact with each other. It is an essential step in system biology to uncover these biochemical interactions and organize them into a regulatory network. By inferring a regulatory network, which may contain a large number of components, scientists obtain a wider view of the biological system and a better understanding of its dynamic nature. With the availability of regulatory networks, scientists are able to answer questions such as: "how does a specific biological system respond to external stimulus or treatment," "what is the stable state of a cellular process under certain conditions," and "how will a biological process behave if some portion of the system were abnormal?" With the insights gained from regulatory network reconstruction, scientists have the ability to control and optimize biological systems, which leads to many practical applications in biotechnology and medicine.

关键词：寿命；复杂系统；生物医学

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