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

Truyen Tran, Tu Dinh Nguyen, Dinh Phung, Svetha Venkatesh

Research output: Contribution to journalArticleResearchpeer-review

39 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)96-105
Number of pages10
JournalJournal of Biomedical Informatics
Volume54
DOIs
Publication statusPublished - Apr 2015
Externally publishedYes

Keywords

  • Electronic medical records
  • Feature grouping
  • Medical objects embedding
  • Suicide risk stratification
  • Vector representation

Cite this

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abstract = "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.",
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Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM). / Tran, Truyen; Nguyen, Tu Dinh; Phung, Dinh; Venkatesh, Svetha.

In: Journal of Biomedical Informatics, Vol. 54, 04.2015, p. 96-105.

Research output: Contribution to journalArticleResearchpeer-review

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