Paper Info
Title
Autonomous Decentralized Privacy-Enabled Data Preparation Architecture for Multicenter Clinical Observational Research
Abstract
Tailoring treatment and clinical decision making to a person's unique characteristics is the next milestone for healthcare informatics, but for it to be accomplished, big data analytics for identifying risk factors and other hidden patterns among patients become paramount. In future these analytics will take the form of multicenter observational research, for which data preparation is vital. Specifically, quality data must be obtained in a timely manner while protecting the privacy of patients in the health records shared among researchers. Furthermore, the coordination and cooperation of a fluctuating number of medical data sources containing these records for clinical data distribution is an additional requirement in multicenter studies. Thus, we propose an autonomous decentralized, privacy-enabled data preparation architecture and novel SEDTM algorithm to meet these requirements, censuring sensitive information via filtration, and extracting relevant clinical data with a fully automated approach. Our evaluation demonstrates a 40% - 60% increase in the retrieval of quality patient data, compared to traditional semantic similarity, for our proposed SEDTM algorithm.
Year
DOI
Venue
2017
10.1109/ISADS.2017.38
2017 IEEE 13th International Symposium on Autonomous Decentralized System (ISADS)
Keywords
Field
DocType
Semantic Similarity,multi-center observational research,privacy-enabled data preparation
Data science,Semantic similarity,Observational study,Data quality,Computer science,Information privacy,Analytics,Health informatics,Information sensitivity,Big data
Conference
ISBN
Citations 
PageRank 
978-1-5090-4043-8
0
0.34
References 
Authors
12
5
Name
Order
Citations
PageRank
Khalid Mahmood Malik183.60
Varun Sathyan200.68
Hisham Kanaan311.06
Ghaus M. Malik422.08
Hafiz Malik518323.47