Inferring multimodal latent topics from electronic health records

Electronic Health Records (EHR) are subject to noise, biases and missing data. Here, the authors present MixEHR, a multi-view Bayesian framework related to collaborative filtering and latent topic models for EHR data integration and modeling.

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Detalles Bibliográficos
Autores principales:	Yue Li, Pratheeksha Nair, Xing Han Lu, Zhi Wen, Yuening Wang, Amir Ardalan Kalantari Dehaghi, Yan Miao, Weiqi Liu, Tamas Ordog, Joanna M. Biernacka, Euijung Ryu, Janet E. Olson, Mark A. Frye, Aihua Liu, Liming Guo, Ariane Marelli, Yuri Ahuja, Jose Davila-Velderrain, Manolis Kellis
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/f93fdf7cc75646b5afb46727b4bdaa9e
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spelling	oai:doaj.org-article:f93fdf7cc75646b5afb46727b4bdaa9e2021-12-02T15:45:13ZInferring multimodal latent topics from electronic health records10.1038/s41467-020-16378-32041-1723https://doaj.org/article/f93fdf7cc75646b5afb46727b4bdaa9e2020-05-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-16378-3https://doaj.org/toc/2041-1723Electronic Health Records (EHR) are subject to noise, biases and missing data. Here, the authors present MixEHR, a multi-view Bayesian framework related to collaborative filtering and latent topic models for EHR data integration and modeling.Yue LiPratheeksha NairXing Han LuZhi WenYuening WangAmir Ardalan Kalantari DehaghiYan MiaoWeiqi LiuTamas OrdogJoanna M. BiernackaEuijung RyuJanet E. OlsonMark A. FryeAihua LiuLiming GuoAriane MarelliYuri AhujaJose Davila-VelderrainManolis KellisNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-17 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Yue Li Pratheeksha Nair Xing Han Lu Zhi Wen Yuening Wang Amir Ardalan Kalantari Dehaghi Yan Miao Weiqi Liu Tamas Ordog Joanna M. Biernacka Euijung Ryu Janet E. Olson Mark A. Frye Aihua Liu Liming Guo Ariane Marelli Yuri Ahuja Jose Davila-Velderrain Manolis Kellis Inferring multimodal latent topics from electronic health records
description	Electronic Health Records (EHR) are subject to noise, biases and missing data. Here, the authors present MixEHR, a multi-view Bayesian framework related to collaborative filtering and latent topic models for EHR data integration and modeling.
format	article
author	Yue Li Pratheeksha Nair Xing Han Lu Zhi Wen Yuening Wang Amir Ardalan Kalantari Dehaghi Yan Miao Weiqi Liu Tamas Ordog Joanna M. Biernacka Euijung Ryu Janet E. Olson Mark A. Frye Aihua Liu Liming Guo Ariane Marelli Yuri Ahuja Jose Davila-Velderrain Manolis Kellis
author_facet	Yue Li Pratheeksha Nair Xing Han Lu Zhi Wen Yuening Wang Amir Ardalan Kalantari Dehaghi Yan Miao Weiqi Liu Tamas Ordog Joanna M. Biernacka Euijung Ryu Janet E. Olson Mark A. Frye Aihua Liu Liming Guo Ariane Marelli Yuri Ahuja Jose Davila-Velderrain Manolis Kellis
author_sort	Yue Li
title	Inferring multimodal latent topics from electronic health records
title_short	Inferring multimodal latent topics from electronic health records
title_full	Inferring multimodal latent topics from electronic health records
title_fullStr	Inferring multimodal latent topics from electronic health records
title_full_unstemmed	Inferring multimodal latent topics from electronic health records
title_sort	inferring multimodal latent topics from electronic health records
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/f93fdf7cc75646b5afb46727b4bdaa9e
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Inferring multimodal latent topics from electronic health records

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