检索结果-内蒙古大学图书馆

Using Pharmacological Neuroimaging to Understand Microcircuit E/I Imbalance in Humans

BIOLOGICAL PSYCHIATRY 2018年第9期83卷 S38-S38页

作者： Anticevic, Alan Schleifer, Charles Adkinson, Brendan Cho, Youngsun Morgan, Peter Savic, Aleksandar Demirtas, Murat Ji, Jie Lisa Murray, John Yale Univ New Haven CT 06520 USA Yale Univ Sch Med New Haven CT 06520 USA Univ Zagreb Zagreb Croatia

来源：评论

学校读者我要写书评

暂无评论

Innovation in Big data analytics --Applications of Mathematical Programming in Medicine and Healthcare

Innovation in Big Data Analytics --Applications of Mathemati...

引用

IEEE International Conference on Big data

作者： EVA K. LEE Industrial and Systems Engineering Georgia Institute of Technology Atlanta GA USA

Risk and decision models and predictive analytics have long been cornerstones for advancement of business analytics in industrial, government, and military applications. In particular, multi-source data system modeling and big data analytics and technologies play an increasingly important role in modern business enterprise. Many problems arising in these domains can be formulated into mathematical models and can be analyzed using sophisticated optimization, decision analysis, and computational techniques. In this talk, we will share some of our successes in healthcare, defense, and service sector applications through innovation in predictive and big data analytics through the modeling and computational advances in integer programming. Specifically, the first model is a discrete support vector machine predictive model that incorporates comprehensive factors related to demographics and socioeconomic status, clinical and hospital resources, operations and utilization, and patient complaints and risk factors for global prediction of readmission and treatment outcome of patients. The second model describes an outcome-driven personalized treatment planning model for cancer patients.

关键词： data-driven analytics integer programing predictive models classification machine learning discrete support vector machine personalized treatment planning cancer multi-objective optimization

来源：评论

学校读者我要写书评

暂无评论

Integrating PMU-data-driven and Physics-based analytics for Power Systems Operations 48

Integrating PMU-data-driven and Physics-based Analytics for ...

引用

48h Asilomar Conference on Signals, Systems and Computers

作者： Chen, Yang Xie, Le Kumar, P. R. Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

ISBN: (纸本)9781479982974

This paper reports our recent work on dimensionality reduction of synchrophasor data and subsequent engineering analysis of the results. Principal component analysis (PCA) based dimensionality reduction is first applied to explore the underlying dimensionality of power systems from the data of massively deployed PMUs. Then the physical interpretations are provided with the power engineering insights: spatial interpretation suggests the coherency of generator groups;temporal analysis indicates the time-scale hierarchy of power system operations. Numerical examples using both synthetic and realistic PMU data are conducted to illustrate the potential value of combining PMU data-driven and physics-based analytics in real-time grid operations.

关键词： Dimensionality reduction phasor measurement unit data-driven analytics physics-based analytics principal component analysis

来源：评论

学校读者我要写书评

暂无评论

Automatic summarization of risk factors preceding disease progression an insight-driven healthcare service case study on using medical records of diabetic patients

引用

WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS 2015年第4期18卷 1163-1175页

作者： Hsueh, Pei-Yun S. Zhu, Xin Xin Hsiao, Mark J. H. Lee, Selina Y. F. Deng, Vincent Ramakrishnan, Sreeram IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA IBM Res Collaboratory Taipei Taiwan

In this study we consider the problem of how to derive insight from medical records to define and improve healthcare services. As noted in many guidelines, risk factors are important to determining the care plan of chronic disease patients, e.g., pre-diabetic or diabetic patients who have started on hemoglobin A1c (HbA1c) control medications. Whereas the traditional management of chronic disease relies on a predetermined set of risk factors, without regard to patient-specific status, literature and recently released guidelines have suggested a less-prescriptive approach that allows flexibility in disease management plans to account for patient-centric information shown in medical records. However, methods of systematically summarizing medical records into risk factors have not been evaluated to support such a patient-centric focus in healthcare services. In this study, we evaluated automatic methods that can identify risk factors important for classifying Diabetic patients at risk of worsen disease progression. In particular, we used the prescription of cardiovascular disease (CVD) medication as the indicator of CVD co-morbidity development in Diabetic patients. We evaluated the summaries obtained with different sources of health information on the risk stratification task and examined the quality of the generated summaries using various proposed intrinsic metrics. In addition, we evaluated to what extent we can reduce the whole medical records into a small set of risk factors. The evaluation illustrates the potential of risk factor summarization and hints on how it can be used to enable practitioners in care planning and to support complex follow-up services at both the point of care and the extended care settings.

关键词： Healthcare service design Case study Disease management data-driven analytics Medical record summarization

来源：评论

学校读者我要写书评

暂无评论

FluMapper: A cyberGIS application for interactive analysis of massive location-based social media

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2014年第13期26卷 2253-2265页

作者： Padmanabhan, Anand Wang, Shaowen Cao, Guofeng Hwang, Myunghwa Zhang, Zhenhua Gao, Yizhao Soltani, Kiumars Liu, Yan Univ Illinois CyberInfrastruct & Geospatial Informat Lab Champaign IL 61820 USA Univ Illinois Dept Geog & Geog Informat Sci Champaign IL USA Univ Illinois CyberGIS Ctr Adv Digital & Spatial Studies Champaign IL USA Univ Illinois Natl Ctr Supercomp Applicat NCSA Champaign IL USA Univ Illinois Illinois Informat Inst Champaign IL USA Texas Tech Univ Dept Geosci Lubbok TX USA Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China

Social media have experienced a spectacular rise in popularity, attracting hundreds of millions of users and generating unprecedented amount of content that increasingly contain location and place information. Collectively, the massive location information in these data provides an excellent opportunity to better understand many geographic phenomena and geospatial dynamics in a timely fashion. Recent studies capitalizing on social networking and media data show significant societal impacts in many areas including prediction of stock market and infectious disease surveillance. However, because location-based social media data are often massive, generated dynamically, and unstructured, significant computation, data, and visualization challenges need to be resolved. This research aims to demonstrate the use of massive social media data to interactively analyze spatiotemporal events across spatial and temporal scales, by establishing a data-driven framework using cyberGISgeographic information systems (GIS) based on advanced cyberinfrastructureto resolve aforementioned challenges. Specifically, FluMapperan application on the CyberGIS Gatewayis employed as a case study to demonstrate the data-driven framework and seamless integration of massive location-based social media data and spatial analytical services within the online problem solving environment of the Gateway. FluMapper presents integrated results from two complementary spatial analyses: (i) an interactive exploration of spatial distribution of flu risk and (ii) dynamic mapping of movement patterns, across multiple spatial, and temporal scales. The seamless integration of these two analyses through the framework illustrates the potential of cyberGIS to resolve the compute and data challenges of analyzing near real-time social media data in an efficient and scalable manner and to support interactive visualization. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： CyberGIS data-driven analytics social media exploratory spatial data analysis

来源：评论

学校读者我要写书评

暂无评论

Process for Measuring and Visualizing an Open Innovation Platform: Case Demola

Process for Measuring and Visualizing an Open Innovation Pla...

引用

17th International Academic MindTrek Conference on Making Sense of Converging Media

作者： Huhtamaki, Jukka Luotonen, Ville Kairamo, Ville Still, Kaisa Russell, Martha G. Tampere Univ Technol Korkeakoulunkatu 3 Tampere 33720 Finland Hermia Oy Tampere 33210 Finland VTT Tech Res Ctr Finland Oulu 90571 Finland MediaX Stanford Stanford CA 94305 USA

ISBN: (纸本)9781450319928

Open innovation breaks the traditional pattern for developing new innovation leading to new business and the activities toward it. Consequently, new requirements are posed to innovation measurement. Demola is an open innovation platform that takes real-life problems from companies and other organizations and puts together and facilitates projects where students from different universities come together to solve the problems. This paper describes a set of network visualizations and animations that were developed in co-creation with the Demola operators to make visible the activity that Demola has initiated. Moreover, the development process used to design the visualizations and the technical process that was applied are described and discussed. We claim that static network visualizations and animations of an open innovation platform development are useful in presenting, describing, marketing and selling the platform for existing and new stakeholders. Our experience shows that in order to develop visualizations and animations that meet the requirements set by the different stakeholders, an iterative and incremental development process is needed. Moreover, we claim that taking a data-driven approach to visualization development is a key enabler in supporting the development.

关键词： Innovation ecosystems co-creation open innovation information visualization dynamic network analysis animation data-driven analytics

来源：评论

学校读者我要写书评

暂无评论

Process for Measuring and Visualizing an Open Innovation Platform: Case Demola 13

Process for Measuring and Visualizing an Open Innovation Pla...

引用

Proceedings of International Conference on Making Sense of Converging Media

作者： Jukka Huhtamäki Ville Luotonen Ville Kairamo Kaisa Still Martha G. Russell Tampere University of Technology Korkeakoulunkatu 3 33720 Tampere Finland +358 40 5854771 Hermia Oy New Factory Väinö Linnan aukio 15 33210 Tampere Finland +358 44 5741937 Hermia Oy New Factory Väinö Linnan aukio 15 33210 Tampere Finland +*** VTT Technical Research Centre of Finland Kaitoväylä 1 90571 Oulu Finland +358 40 1508741 mediaX at Stanford Cordura Hall 210 Panama Street Stanford CA 94305-4115 +1 650 7231616

ISBN: (纸本)9781450319928

关键词： dynamic network analysis information visualization animation co-creation open innovation data-driven analytics Innovation ecosystems

来源：评论

学校读者我要写书评

暂无评论

From Clinical Requirement to Personalized Wellness Decision Support: A data-driven Framework for Computer-Supported Guideline Refinement

From Clinical Requirement to Personalized Wellness Decision ...

引用

24th Medical Informatics in Europe Conference (MIE)

作者： Hsueh, Pei-Yun Lan, Ci-Wei Deng, Vincent Zhu, Xinxin IBM TJ Watson Res Ctr Ossining NY 10598 USA IBM Taiwan Res Collaboratory Taipei Taiwan

ISBN: (纸本)9781614991014

Personalized wellness decision support has gained significant attention, owing to the shift to a patient-centric paradigm in healthcare domains, and the consequent availability of a wealth of patient-related data. Despite the success of data-driven analytics in improving practice outcome, there is a gap towards their deployment in guideline-based practice. In this paper we report on findings related to computer-supported guideline refinement, which maps a patient's guideline requirements to personalized recommendations that suit the patient's current context. In particular, we present a novel data-driven personalization framework, casting the mapping task as a statistical decision problem in search of a solution to maximize expected utility. The proposed framework is well suited to produce personalized recommendations based on not only clinical factors but contextual factors that reflect individual differences in non-clinical settings. We then describe its implementation within the guideline-based clinical decision support system and discuss opportunities and challenges looking forward.

关键词： Clinical decision support guideline refinement data-driven analytics

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：