NDLI: Usage Patterns and Data Quality: A Case Study of a National Type-1 Diabetes Study

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Usage Patterns and Data Quality: A Case Study of a National Type-1 Diabetes Study

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Usage Patterns and Data Quality: A Case Study of a National Type-1 Diabetes Study

Content Provider	ACM Digital Library
Author	Sinnott, Richard O. Effendy, Jemie Glöckner, Stephan Hu, William Wu, Jinrong Li, Jiajie
Abstract	The Environmental Determinants of Islet Auto- immunity (ENDIA) project is Australia's largest study into the causes of Type-1 Diabetes (T1D). The ENDIA study is supported by a Cloud-based software platform including a clinical registry comprising extensive longitudinal information on families at risk of having a child that might go on to develop T1D. This registry includes both demographic and clinical information on families and children as well as the environmental factors that might influence the onset of T1D. A multitude of samples are obtained through the study and used to support a diverse portfolio of bioinformatics data analytics. The quality of the data in the registry is essential to the overall success of the project. This paper presents a Cloud-based log-analytics platform that supports the detailed analysis of patterns of usage of the registry by the clinical centres and collaborators involved. We explore the impact that the usage patterns have on the overall data quality. We also consider ways of improving data quality by mothers entering their own data through targeted mobile applications that have been developed for dietary data collection.
Starting Page	18
Ending Page	27
Page Count	10
File Format	PDF
ISBN	9781450352246
DOI	10.1145/3107514.3107518
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-05-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cloud Log analysis Type-1 diabetes Auditing
Content Type	Text
Resource Type	Article

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