The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
Abhijit Chaudhury - One of the best experts on this subject based on the ideXlab platform.
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an empirical investigation of Decision making satisfaction in web based Decision Support Systems
Decision Support Systems, 2004Co-Authors: Pratyush Bharati, Abhijit ChaudhuryAbstract:Web-based information Systems are increasingly being used for Decision Support applications. However, few empirical studies have been conducted on web-based Decision Support Systems (DSS). This experimental research endeavors to understand factors that impact Decision-making satisfaction in web-based Decision Support Systems. Using structural equation modeling (SEM) approach, the analysis reveals that information quality and system quality influence Decision-making satisfaction, while information presentation does not have an effect on Decision-making satisfaction.
Philippe Lambin - One of the best experts on this subject based on the ideXlab platform.
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Decision Support Systems for personalized and participative radiation oncology
Advanced Drug Delivery Reviews, 2017Co-Authors: Philippe Lambin, Jaap D Zindler, Ben G L Vanneste, Lien Van De Voorde, Danielle B P Eekers, Inge Compter, Kranthi Marella Panth, Jurgen Peerlings, Ruben T H M Larue, Timo M DeistAbstract:A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being Supported by the development of clinical Decision Support Systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, Decision Support Systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, Decision Support Systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.
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predicting outcomes in radiation oncology multifactorial Decision Support Systems
Nature Reviews Clinical Oncology, 2013Co-Authors: Philippe Lambin, Ruud G P M Van Stiphout, Maud H W Starmans, Emmanuel Riosvelazquez, Georgi Nalbantov, Hugo J W L Aerts, Erik Roelofs, Wouter Van Elmpt, Paul C Boutros, Pierluigi GranoneAbstract:With the emergence of individualized medicine and the increasing amount and complexity of available medical data, a growing need exists for the development of clinical Decision-Support Systems based on prediction models of treatment outcome. In radiation oncology, these models combine both predictive and prognostic data factors from clinical, imaging, molecular and other sources to achieve the highest accuracy to predict tumour response and follow-up event rates. In this Review, we provide an overview of the factors that are correlated with outcome-including survival, recurrence patterns and toxicity-in radiation oncology and discuss the methodology behind the development of prediction models, which is a multistage process. Even after initial development and clinical introduction, a truly useful predictive model will be continuously re-evaluated on different patient datasets from different regions to ensure its population-specific strength. In the future, validated Decision-Support Systems will be fully integrated in the clinic, with data and knowledge being shared in a standardized, instant and global manner.
Vicki L. Sauter - One of the best experts on this subject based on the ideXlab platform.
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Decision Support Systems for business intelligence
2011Co-Authors: Vicki L. SauterAbstract:I. Introduction to Decision Support Systems. Chapter 1. Introduction. Chapter 2. Decision Making. II. DSS Components. Chapter 3. The Data Component. Chapter 4. The Model Component. Chapter 4S. Intelligence and Decision Support Systems. Chapter 5. The User Interface. III. Issues of Design. Chapter 6. International Decision Support Systems. Chapter 7. Designing a Decision Support System. Chapter 8. Object Oriented Technologies and Decision Support Systems Design. Chapter 9. Implementation Strategy. IV. Extension of Decision Support Systems. Chapter 10. Executive Systems and Dashboards. Chapter 11. Group Decision Support Systems.
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Decision Support Systems for Business Intelligence: Second Edition
Decision Support Systems for Business Intelligence: Second Edition, 2011Co-Authors: Vicki L. SauterAbstract:Praise for the First Edition "This is the most usable Decision Support Systems text. [i]t is far better than any other text in the field" -Computing Reviews Computer-based Systems known as Decision Support Systems (DSS) play a vital role in helping professionals across various fields of practice understand what information is needed, when it is needed, and in what form in order to make smart and valuable business Decisions. Providing a unique combination of theory, applications, and technology, Decision Support Systems for Business Intelligence, Second Edition supplies readers with the hands-on approach that is needed to understand the implications of theory to DSS design as well as the skills needed to construct a DSS. This new edition reflects numerous advances in the field as well as the latest related technological developments. By addressing all topics on three levels-general theory, implications for DSS design, and code development-the author presents an integrated analysis of what every DSS designer needs to know. This Second Edition features: • Expanded coverage of data mining with new examples • Newly added discussion of business intelligence and transnational corporations • Discussion of the increased capabilities of databases and the significant growth of user interfaces and models • Emphasis on analytics to encourage DSS builders to utilize sufficient modeling Support in their Systems • A thoroughly updated section on data warehousing including architecture, data adjustment, and data scrubbing • Explanations and implications of DSS differences across cultures and the challenges associated with transnational Systems Each chapter discusses various aspects of DSS that exist in real-world applications, and one main example of a DSS to facilitate car purchases is used throughout the entire book. Screenshots from JavaScript® and Adobe® ColdFusion are presented to demonstrate the use of popular software packages that carry out the discussed techniques, and a related Web site houses all of the book's figures along with demo versions of Decision Support packages, additional examples, and links to developments in the field. Decision Support Systems for Business Intelligence, Second Edition is an excellent book for courses on information Systems, Decision Support Systems, and data mining at the advanced undergraduate and graduate levels. It also serves as a practical reference for professionals working in the fields of business, statistics, engineering, and computer technology. © 2010 John Wiley & Sons, Inc. All rights reserved.
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Decision Support Systems: An Applied Managerial Approach
1996Co-Authors: Vicki L. SauterAbstract:INTRODUCTION TO Decision Support Systems. Decision Making. COMPONENTS OF A DSS. Data Components. Model Components. Intelligence and Decision Support Systems. User-Interface Components. Mail Components. ISSUES OF DESIGN. International Decision Support Systems. Designing a Decision Support System. Object-Oriented Technologies and Decision Support Systems Design. Implementation and Evaluation of Decision Support Systems. RELATED Systems. Group Decision Support Systems. Executive Information Systems. Photo Credits. Index.
Timo M Deist - One of the best experts on this subject based on the ideXlab platform.
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Decision Support Systems for personalized and participative radiation oncology
Advanced Drug Delivery Reviews, 2017Co-Authors: Philippe Lambin, Jaap D Zindler, Ben G L Vanneste, Lien Van De Voorde, Danielle B P Eekers, Inge Compter, Kranthi Marella Panth, Jurgen Peerlings, Ruben T H M Larue, Timo M DeistAbstract:A paradigm shift from current population based medicine to personalized and participative medicine is underway. This transition is being Supported by the development of clinical Decision Support Systems based on prediction models of treatment outcome. In radiation oncology, these models 'learn' using advanced and innovative information technologies (ideally in a distributed fashion - please watch the animation: http://youtu.be/ZDJFOxpwqEA) from all available/appropriate medical data (clinical, treatment, imaging, biological/genetic, etc.) to achieve the highest possible accuracy with respect to prediction of tumor response and normal tissue toxicity. In this position paper, we deliver an overview of the factors that are associated with outcome in radiation oncology and discuss the methodology behind the development of accurate prediction models, which is a multi-faceted process. Subsequent to initial development/validation and clinical introduction, Decision Support Systems should be constantly re-evaluated (through quality assurance procedures) in different patient datasets in order to refine and re-optimize the models, ensuring the continuous utility of the models. In the reasonably near future, Decision Support Systems will be fully integrated within the clinic, with data and knowledge being shared in a standardized, dynamic, and potentially global manner enabling truly personalized and participative medicine.
Pratyush Bharati - One of the best experts on this subject based on the ideXlab platform.
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an empirical investigation of Decision making satisfaction in web based Decision Support Systems
Decision Support Systems, 2004Co-Authors: Pratyush Bharati, Abhijit ChaudhuryAbstract:Web-based information Systems are increasingly being used for Decision Support applications. However, few empirical studies have been conducted on web-based Decision Support Systems (DSS). This experimental research endeavors to understand factors that impact Decision-making satisfaction in web-based Decision Support Systems. Using structural equation modeling (SEM) approach, the analysis reveals that information quality and system quality influence Decision-making satisfaction, while information presentation does not have an effect on Decision-making satisfaction.
