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In today's rapidly changing educational and business climate, organizational transformation has become a key area of development for many different and varied environments, both commercial and academic. This book addresses issues related to developing Decision Support Systems (DSS) which are sensitive and adaptable to different contexts and evolving technical and work environments. In addition to addressing the various cultural/social, organizational/individual, task/technology contexts of DSS, the book also anchors these discussions in a practical context, drawing on case studies to illustrate the theoretical dimensions stressed. This book includes the following issues: Frameworks for understanding the contexts and environments of decision support; Cases and issues in decision support and organizational transformation in context; An inter-disciplinary analysis of DSS, covering a wide variety of situations; and Real-world applications of DSS . It contains selected papers presented and discussed at the International Conference on Context-Sensitive Decision Support Systems, which was sponsored by the International Federation for Information Processing (IFIP) and held in Bled, Slovenia in July 1998. The book will prove invaluable to anyone working in information and decision support systems development, management, implementation and evaluation, as well as to researchers/practitioners in organizational analysis and development, management and business administration, sociology and psychology of organizations, human relations and human factors management.
As the most comprehensive reference work dealing with decision support systems (DSS), this book is essential for the library of every DSS practitioner, researcher, and educator. Written by an international array of DSS luminaries, it contains more than 70 chapters that approach decision support systems from a wide variety of perspectives. These range from classic foundations to cutting-edge thought, informative to provocative, theoretical to practical, historical to futuristic, human to technological, and operational to strategic. The chapters are conveniently organized into ten major sections that novices and experts alike will refer to for years to come.
This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience.
This book will be bought by researchers and graduates students in Artificial Intelligence and management as well as practising managers and consultants interested in the application of IT and information systems in real business environment.
This reference demonstrates the development of a context aware decision-making health informatics system with the objective to automate the analysis of human centric wellness and assist medical decision-making in healthcare. The book introduces readers to the basics of a clinical decision support system. This is followed by chapters that explain how to analyze healthcare data for anomaly detection and clinical correlations. The next two sections cover machine learning techniques for object detection and a case study for hemorrhage detection. These sections aim to expand the understanding of simple and advanced neural networks in health informatics. The authors also explore how machine learning model choices based on context can assist medical professionals in different scenarios. Key Features : -Reader-friendly format with clear headings, introductions and summaries in each chapter -Detailed references for readers who want to conduct further research -Expert contributors providing authoritative knowledge on machine learning techniques and human-centric wellness -Practical applications of data science in healthcare designed to solve problems and enhance patient wellbeing -Deep learning use cases for different medical conditions including hemorrhages, gallbladder stones and diabetic retinopathy Demonstrations of fast and efficient CNN models with varying parameters such as Single shot detector, R-CNN, Mask R-CNN, modified contrast enhancement and improved LSTM models. This reference is intended as a primary resource for professionals, researchers, software developers and technicians working in healthcare informatics systems and medical diagnostics. It also serves as a supplementary resource for learners in bioinformatics, biomedical engineering and medical informatics programs and anyone who requires technical knowledge about algorithms in medical decision support systems.
The two-volume set LNCS 10273 and 10274 constitutes the refereed proceedings of the thematic track on Human Interface and the Management of Information, held as part of the 19th HCI International 2017, in Vancouver, BC, Canada, in July 2017. HCII 2017 received a total of 4340 submissions, of which 1228 papers were accepted for publication after a careful reviewing process. The 102 papers presented in these volumes were organized in topical sections as follows: Part I: Visualization Methods and Tools; Information and Interaction Design; Knowledge and Service Management; Multimodal and Embodied Interaction. Part II: Information and Learning; Information in Virtual and Augmented Reality; Recommender and Decision Support Systems; Intelligent Systems; Supporting Collaboration and User Communities; Case Studies.
This book constitutes the refereed post-conference proceedings of the International Conferences ICCASA and ICTCC 2020, held in November 2020 in Thai Nguyen, Vietnam. The 27 revised full papers presented were carefully selected from 68 submissions. The papers of ICCASA cover a wide spectrum in the area of context-aware-systems. CAS is characterized by its self- facets such as self-organization, self-configuration, self-healing, self-optimization, self-protection used to dynamically control computing and networking functions. The papers of ICTCC cover formal methods for self-adaptive systems and discuss natural approaches and techniques for computation and communication.
This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.
Decision support systems have experienced a marked increase in attention and importance over the past 25 years. The aim of this book is to survey the decision support system (DSS) field – covering both developed territory and emergent frontiers. It will give the reader a clear understanding of fundamental DSS concepts, methods, technologies, trends, and issues. It will serve as a basic reference work for DSS research, practice, and instruction. To achieve these goals, the book has been designed according to a ten-part structure, divided in two volumes with chapters authored by well-known, well-versed scholars and practitioners from the DSS community.
Decision Support Systems (DSS) are typically viewed as computerized aids for managerial decision making at the individual level. By contrast, this volume focuses on DSS for group and organizational decision making. The contributions are from academics and practitioners who are developing new concepts, techniques and software to support decision-related activities involving multiple participants. The book is in five sections. The first looks at the organizational setting of group decision support systems and experiences of various enterprises in their use. The second section is concerned with supporting negotiation processes, decision situations where the participants have conflicting goals. The third section deals with analysis and design issues in developing group/organizational DSS. Behavioral issues relating to the impact of the technology are examined in section four, while the fifth section discusses practical experiences with group decision rooms'.