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Deepr: a convolutional net for medical records

Nguyen, Phuoc, Tran, Truyen, Wickramasinghe, Nilmini and Venkatesh, Svetha 2017, Deepr: a convolutional net for medical records, IEEE journal of biomedical and health informatics, vol. 21, no. 1, pp. 22-30, doi: 10.1109/JBHI.2016.2633963.

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Title Deepr: a convolutional net for medical records
Author(s) Nguyen, PhuocORCID iD for Nguyen, Phuoc orcid.org/0000-0002-1649-2519
Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Wickramasinghe, Nilmini
Venkatesh, Svetha
Journal name IEEE journal of biomedical and health informatics
Volume number 21
Issue number 1
Start page 22
End page 30
Total pages 9
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2017-01
ISSN 2168-2194
Keyword(s) convolutional neural networks
deep learning
medical records
Summary Feature engineering remains a major bottleneck when creating predictive systems from electronic medical records. At present, an important missing element is detecting predictive regular clinical motifs from irregular episodic records. We present Deepr (short for Deep record), a new end-to-end deep learning system that learns to extract features from medical records and predicts future risk automatically. Deepr transforms a record into a sequence of discrete elements separated by coded time gaps and hospital transfers. On top of the sequence is a convolutional neural net that detects and combines predictive local clinical motifs to stratify the risk. Deepr permits transparent inspection and visualization of its inner working. We validate Deepr on hospital data to predict unplanned readmission after discharge. Deepr achieves superior accuracy compared to traditional techniques, detects meaningful clinical motifs, and uncovers the underlying structure of the disease and intervention space.
Language eng
DOI 10.1109/JBHI.2016.2633963
Field of Research 111799 Public Health and Health Services not elsewhere classified
Socio Economic Objective 970111 Expanding Knowledge in the Medical and Health Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30092392

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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.