NDLI: Tensor-Factorization-Based Phenotyping using Group Information: Case Study on the Efficacy of Statins

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Tensor-Factorization-Based Phenotyping using Group Information: Case Study on the Efficacy of Statins

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Tensor-Factorization-Based Phenotyping using Group Information: Case Study on the Efficacy of Statins

Content Provider	ACM Digital Library
Author	Kim, Hun-Sung Kim, Yejin Choi, Jingyun Choi, In Young Yu, Hwanjo
Abstract	To automatically extract medical concepts from raw electronic health records (EHRs), several applications based on machine learning techniques have been proposed. Among the various techniques, tensor factorization methods have attracted considerable attention because tensor representations can capture interactions among high-dimensional EHRs. Most of the existing tensor factorization methods for computational phenotyping are only designed to derive individual phenotypes that approximate the original data. However, deriving grouped phenotypes is desirable because patients form natural groups of interest (i.e., efficacy of treatment and disease categories). In this paper, we propose Supervised Non-negative Tensor Factorization with Multinomial Logistic Regression (SNTFL) to derive grouped phenotypes that are discriminative. We define a discriminative constraint to derive grouped phenotypes and jointly optimize a multinomial logistic regression during the tensor factorization process. Our case study on a hyperlipidemia dataset demonstrates that our proposed method obtains better discrimination on patient groups compared to the baselines and successfully discovers meaningful patient subgroups.
Starting Page	516
Ending Page	525
Page Count	10
File Format	PDF
ISBN	9781450347228
DOI	10.1145/3107411.3107423
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Representation learning Joint learning Computational phenotyping
Content Type	Text
Resource Type	Article

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