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Title: Community cloud architecture to improve use accessibility with security compliance in health big data applications
The adoption of big data analytics in healthcare applications is overwhelming not only because of the huge volume of data being analyzed, but also because of the heterogeneity and sensitivity of the data. Effective and efficient analysis and visualization of secure patient health records are needed to e.g., find new trends in disease management, determining risk factors for diseases, and personalized medicine. In this paper, we propose a novel community cloud architecture to help clinicians and researchers to have easy/increased accessibility to data sets from multiple sources, while also ensuring security compliance of data providers is not compromised. Our cloud-based system design configuration with cloudlet principles ensures application performance has high-speed processing, and data analytics is sufficiently scalable while adhering to security standards (e.g., HIPAA, NIST). Through a case study, we show how our community cloud architecture can be implemented along with best practices in an ophthalmology case study which includes health big data (i.e., Health Facts database, I2B2, Millennium) hosted in a campus cloud infrastructure featuring virtual desktop thin-clients and relevant Data Classification Levels in storage.  more » « less
Award ID(s):
1827177
NSF-PAR ID:
10119294
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Community cloud architecture to improve use accessibility with security compliance in health big data applications
Page Range / eLocation ID:
377 to 380
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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The breast corpus subset should be released by November 2021. By December 2021 we should also release the unannotated FCCC data. We are currently annotating urinary tract data as well. We expect to release about 5,600 processed TUH slides in this subset. We have an additional 53,000 unprocessed TUH slides digitized. Corpora of this size will stimulate the development of a new generation of deep learning technology. In clinical settings where resources are limited, an assistive diagnoses model could support pathologists’ workload and even help prioritize suspected cancerous cases. ACKNOWLEDGMENTS This material is supported by the National Science Foundation under grants nos. CNS-1726188 and 1925494. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. REFERENCES [1] N. 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