Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)

Tran, Truyen, Nguyen, Tu Dinh, Phung, Dinh and Venkatesh, Svetha 2015, Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM), Journal of biomedical informatics, vol. 54, pp. 96-105, doi: 10.1016/j.jbi.2015.01.012.

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Title Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)
Author(s) Tran, TruyenORCID iD for Tran, Truyen orcid.org/0000-0001-6531-8907
Nguyen, Tu Dinh
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Journal name Journal of biomedical informatics
Volume number 54
Start page 96
End page 105
Total pages 10
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2015-04
ISSN 1532-0480
Keyword(s) Electronic medical records
Feature grouping
Medical objects embedding
Suicide risk stratification
Vector representation
Science & Technology
Life Sciences & Biomedicine
Computer Science, Interdisciplinary Applications
Computer Science
Summary Electronic medical record (EMR) offers promises for novel analytics. However, manual feature engineering from EMR is labor intensive because EMR is complex - it contains temporal, mixed-type and multimodal data packed in irregular episodes. We present a computational framework to harness EMR with minimal human supervision via restricted Boltzmann machine (RBM). The framework derives a new representation of medical objects by embedding them in a low-dimensional vector space. This new representation facilitates algebraic and statistical manipulations such as projection onto 2D plane (thereby offering intuitive visualization), object grouping (hence enabling automated phenotyping), and risk stratification. To enhance model interpretability, we introduced two constraints into model parameters: (a) nonnegative coefficients, and (b) structural smoothness. These result in a novel model called eNRBM (EMR-driven nonnegative RBM). We demonstrate the capability of the eNRBM on a cohort of 7578 mental health patients under suicide risk assessment. The derived representation not only shows clinically meaningful feature grouping but also facilitates short-term risk stratification. The F-scores, 0.21 for moderate-risk and 0.36 for high-risk, are significantly higher than those obtained by clinicians and competitive with the results obtained by support vector machines.
Language eng
DOI 10.1016/j.jbi.2015.01.012
Field of Research 080109 Pattern Recognition and Data Mining
06 Biological Sciences
08 Information And Computing Sciences
11 Medical And Health Sciences
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2015, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30076875

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