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Discriminative and Distinct Phenotyping by Constrained Tensor Factorization

Adoption of Electronic Health Record (EHR) systems has led to collection of massive healthcare data, which creates oppor- tunities and challenges to study them. Computational phenotyping offers a promising way to convert the sparse and complex data into meaningful concepts that are interpretable to...

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Autores principales: Kim, Yejin, El-Kareh, Robert, Sun, Jimeng, Yu, Hwanjo, Jiang, Xiaoqian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5430728/
https://www.ncbi.nlm.nih.gov/pubmed/28442772
http://dx.doi.org/10.1038/s41598-017-01139-y
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author Kim, Yejin
El-Kareh, Robert
Sun, Jimeng
Yu, Hwanjo
Jiang, Xiaoqian
author_facet Kim, Yejin
El-Kareh, Robert
Sun, Jimeng
Yu, Hwanjo
Jiang, Xiaoqian
author_sort Kim, Yejin
collection PubMed
description Adoption of Electronic Health Record (EHR) systems has led to collection of massive healthcare data, which creates oppor- tunities and challenges to study them. Computational phenotyping offers a promising way to convert the sparse and complex data into meaningful concepts that are interpretable to healthcare givers to make use of them. We propose a novel su- pervised nonnegative tensor factorization methodology that derives discriminative and distinct phenotypes. We represented co-occurrence of diagnoses and prescriptions in EHRs as a third-order tensor, and decomposed it using the CP algorithm. We evaluated discriminative power of our models with an Intensive Care Unit database (MIMIC-III) and demonstrated superior performance than state-of-the-art ICU mortality calculators (e.g., APACHE II, SAPS II). Example of the resulted phenotypes are sepsis with acute kidney injury, cardiac surgery, anemia, respiratory failure, heart failure, cardiac arrest, metastatic cancer (requiring ICU), end-stage dementia (requiring ICU and transitioned to comfort-care), intraabdominal conditions, and alcohol abuse/withdrawal.
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spelling pubmed-54307282017-05-16 Discriminative and Distinct Phenotyping by Constrained Tensor Factorization Kim, Yejin El-Kareh, Robert Sun, Jimeng Yu, Hwanjo Jiang, Xiaoqian Sci Rep Article Adoption of Electronic Health Record (EHR) systems has led to collection of massive healthcare data, which creates oppor- tunities and challenges to study them. Computational phenotyping offers a promising way to convert the sparse and complex data into meaningful concepts that are interpretable to healthcare givers to make use of them. We propose a novel su- pervised nonnegative tensor factorization methodology that derives discriminative and distinct phenotypes. We represented co-occurrence of diagnoses and prescriptions in EHRs as a third-order tensor, and decomposed it using the CP algorithm. We evaluated discriminative power of our models with an Intensive Care Unit database (MIMIC-III) and demonstrated superior performance than state-of-the-art ICU mortality calculators (e.g., APACHE II, SAPS II). Example of the resulted phenotypes are sepsis with acute kidney injury, cardiac surgery, anemia, respiratory failure, heart failure, cardiac arrest, metastatic cancer (requiring ICU), end-stage dementia (requiring ICU and transitioned to comfort-care), intraabdominal conditions, and alcohol abuse/withdrawal. Nature Publishing Group UK 2017-04-25 /pmc/articles/PMC5430728/ /pubmed/28442772 http://dx.doi.org/10.1038/s41598-017-01139-y Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Kim, Yejin
El-Kareh, Robert
Sun, Jimeng
Yu, Hwanjo
Jiang, Xiaoqian
Discriminative and Distinct Phenotyping by Constrained Tensor Factorization
title Discriminative and Distinct Phenotyping by Constrained Tensor Factorization
title_full Discriminative and Distinct Phenotyping by Constrained Tensor Factorization
title_fullStr Discriminative and Distinct Phenotyping by Constrained Tensor Factorization
title_full_unstemmed Discriminative and Distinct Phenotyping by Constrained Tensor Factorization
title_short Discriminative and Distinct Phenotyping by Constrained Tensor Factorization
title_sort discriminative and distinct phenotyping by constrained tensor factorization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5430728/
https://www.ncbi.nlm.nih.gov/pubmed/28442772
http://dx.doi.org/10.1038/s41598-017-01139-y
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