Healthcare Collaborative Network Based on Common KADS Methodology

2015 ◽  
Author(s):  
Morcous M.Y. Massoud
Keyword(s):  
Collaborative Network

The Collaborative Network Approach: a model for advancing patient-centric research for Castleman disease and other rare diseases

2019 ◽  
Vol 3 (1) ◽  
pp. 97-105
Author(s):  
Mary Zuccato ◽  
Dustin Shilling ◽  
David C. Fajgenbaum
Keyword(s):  
Rare Diseases ◽  
Treatment Options ◽  
Castleman Disease ◽  
High Impact ◽  
Collaborative Network ◽  
Network Approach ◽  
Grant Applications ◽  
Research Questions ◽  
Novel Strategy ◽  
Patient Centric

Abstract There are ∼7000 rare diseases affecting 30 000 000 individuals in the U.S.A. 95% of these rare diseases do not have a single Food and Drug Administration-approved therapy. Relatively, limited progress has been made to develop new or repurpose existing therapies for these disorders, in part because traditional funding models are not as effective when applied to rare diseases. Due to the suboptimal research infrastructure and treatment options for Castleman disease, the Castleman Disease Collaborative Network (CDCN), founded in 2012, spearheaded a novel strategy for advancing biomedical research, the ‘Collaborative Network Approach’. At its heart, the Collaborative Network Approach leverages and integrates the entire community of stakeholders — patients, physicians and researchers — to identify and prioritize high-impact research questions. It then recruits the most qualified researchers to conduct these studies. In parallel, patients are empowered to fight back by supporting research through fundraising and providing their biospecimens and clinical data. This approach democratizes research, allowing the entire community to identify the most clinically relevant and pressing questions; any idea can be translated into a study rather than limiting research to the ideas proposed by researchers in grant applications. Preliminary results from the CDCN and other organizations that have followed its Collaborative Network Approach suggest that this model is generalizable across rare diseases.

Influential Factors of Collaborative Networks in Manufacturing: Validation of a Conceptual Model

10.28945/2120 ◽  
2015 ◽  
Vol 10 ◽  
pp. 001-019 ◽  
Author(s):  
Danny Wee Hock Quik ◽  
Nevan Wright ◽  
Ammar Rashid ◽  
Sivadass Thiruchelvam
Keyword(s):  
Organizational Support ◽  
Influential Factors ◽  
Perceived Usefulness ◽  
Manufacturing Industries ◽  
Collaborative Networks ◽  
Collaborative Network ◽  
Perceived Effectiveness ◽  
External Stakeholders ◽  
Technical Systems ◽  
External Learning

The purpose of the study is to identify influential factors in the use of collaborative networks within the context of manufacturing. The study aims to investigate factors that influence employees’ learning, and to bridge the gap between theory and praxis in collaborative networks in manufacturing. The study further extends the boundary of a collaborative network beyond enterprises to include suppliers, customers, and external stakeholders. It provides a holistic perspective of collaborative networks within the complexity of the manufacturing environment, based on empirical evidence from a questionnaire survey of 246 respondents from diverse manufacturing industries. Drawing upon the socio-technical systems (STS) theory, the study presents the theoretical context and interpretations through the lens of manufacturing. The results show significant influences of organizational support, promotive interactions, positive interdependence, internal-external learning, perceived effectiveness, and perceived usefulness on the use of collaborative networks among manufacturing employees. The study offers a basis of empirical validity for measuring collaborative networks in organizational learning and knowledge/information sharing in manufacturing.

Collaborative network-based virtual reality

2002 ◽  
Author(s):  
Kenneth E. Hay ◽  
Dean M. Elliott ◽  
Beaumie Kim
Keyword(s):  
Virtual Reality ◽  
Collaborative Network

Blockchain Collaborative Network Development Framework

2019 ◽  
Author(s):  
Arthur Adamopoulos ◽  
Bill Davey ◽  
Vince Bruno ◽  
Martin Dick
Keyword(s):  
Collaborative Network ◽  
Network Development ◽  
Development Framework

scholarly journals Current trends in nontuberculous mycobacteria infections in Canadian children: A Pediatric Investigators Collaborative Network on Infections in Canada (PICNIC) study

2010 ◽  
Vol 15 (5) ◽  
pp. 276-282 ◽  
Author(s):  
Anne Pham-Huy ◽  
Joan L Robinson ◽  
Bruce Tapiéro ◽  
Chantal Bernard ◽  
Sam Daniel ◽  
...  
Keyword(s):  
Nontuberculous Mycobacteria ◽  
Collaborative Network ◽  
Current Trends

scholarly journals Paediatric Investigators Collaborative Network on Infections in Canada (PICNIC) Study of the Epidemiology of Bacterial and Fungal Meningitis in Infants Aged <90 Days

2016 ◽  
Vol 3 (suppl_1) ◽  
Author(s):  
Lynda Ouchenir ◽  
Christian Renaud ◽  
Ari Bitnun ◽  
Sarah Khan ◽  
Michelle Barton ◽  
...  
Keyword(s):  
Collaborative Network ◽  
Fungal Meningitis

Efficient Ontology Integration Model for Better Inference in Context Aware Computing

2011 ◽  
Vol 268-270 ◽  
pp. 841-846
Author(s):  
Soo Mi Yang
Keyword(s):  
Video Data ◽  
Smart Devices ◽  
Collaborative Network ◽  
Context Aware ◽  
Integration Model ◽  
Smart Cameras ◽  
Ontology Integration ◽  
Context Aware Computing ◽  
Heterogeneous Devices ◽  
Distributed Ontology

In this paper, we describe efficient ontology integration model for better context inference based on distributed ontology framework. Context aware computing with inference based on ontology is widely used in distributed surveillance environment. In such a distributed surveillance environment, surveillance devices such as smart cameras may carry heterogeneous video data with different transmission ranges, latency, and formats. However even smart devices, they generally have small memory and power which can manage only part of ontology data. In our efficient ontology integration model, each of agents built in such devices get services not only from a region server, but also peer servers. For such a collaborative network, an effective cache framework that can handle heterogeneous devices is required for the efficient ontology integration. In this paper, we propose a efficient ontology integration model which is adaptive to the actual device demands and that of its neighbors. Our scheme shows the efficiency of model resulted in better context inference.

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