A Data Analytics Pipeline for Smart Healthcare Applications

Chonho Lee (), Seiya Murata (), Kobo Ishigaki and Susumu Date ()
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Chonho Lee: Osaka University, Cybermedia Center
Seiya Murata: Osaka University, Graduate School of Information Science and Technology
Kobo Ishigaki: Osaka University, Graduate School of Information Science and Technology
Susumu Date: Osaka University, Cybermedia Center

A chapter in Sustained Simulation Performance 2017, 2017, pp 181-192 from Springer

Abstract: Abstract The rapidly increasing availability of healthcare data is becoming the driving force for the adoption of data-driven approaches. However, due to a large amount of heterogeneous dataset including images (MRI, X-ray), texts (doctor’s note) and sounds, doctors still struggle against temporal and accuracy limitations when processing and analyzing such big data using conventional machines and approaches. Employing advanced machine learning techniques on big healthcare data anlaytics supported by Petascale high performance computing resources is expected to remove those limitations and help find unseen healthcare insights. This paper introduces a data analytics pipeline consisting of data curation (including cleansing, annotation, and integration) and data analytics processes, necessary to develop smart healthcare applications. In order to show its practical use, we present sample applications such as diagnostic imaging, landmark extraction and casenote generation using deep learning models, for orthodontic treatments in dentistry. Eventually, we will build smart healthcare infrastructure and system that fully automate the set of the curation and analytics processes. The developed system will dramatically reduce doctor’s workload and is smoothly expanded to other fields.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-66896-3_12

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DOI: 10.1007/978-3-319-66896-3_12

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