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Using machine learning to address data accuracy and information integrity in digital health delivery

Sako, Zaid Zekiria, Karpathiou, Vass, Adibi, Sasan and Wickramasinghe, Nilmini 2016, Using machine learning to address data accuracy and information integrity in digital health delivery, in Bled 2016 : Digital Economy : Proceedings of the 29th Bled eConference, Association for Information Systems, Atlanta, Ga., pp. 301-318.

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Title Using machine learning to address data accuracy and information integrity in digital health delivery
Author(s) Sako, Zaid Zekiria
Karpathiou, Vass
Adibi, Sasan
Wickramasinghe, Nilmini
Conference name Bled. eConference (29th : 2016 : Bled, Slovenia)
Conference location Bled, Slovenia
Conference dates 19-22 Jun. 2016
Title of proceedings Bled 2016 : Digital Economy : Proceedings of the 29th Bled eConference
Publication date 2016
Conference series Bled eConference
Start page 301
End page 318
Total pages 18
Publisher Association for Information Systems
Place of publication Atlanta, Ga.
Keyword(s) mHealth
Machine Learning
Data Accuracy
Information Integrity
Summary Today, much of healthcare delivery is digital. In particular, there exists a plethora of mHealth solutions being developed. This in turn necessitates the need for accurate data and information integrity if superior mHealth is to ensue. Lack of data accuracy and information integrity can cause serious harm to patients and limit the benefits of mHealth technology. The described exploratory case study serves to investigate data accuracy and information integrity in mHealth, with the aim of incorporating Machine Learning to detect sources of inaccurate data and deliver quality information.
Language eng
Field of Research 080702 Health Informatics
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2016, The Authors
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30095169

Document type: Conference Paper
Collections: School of Health and Social Development
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.