Inferring multimodal latent topics from electronic health records

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
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Acceso en línea:https://doaj.org/article/f93fdf7cc75646b5afb46727b4bdaa9e
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Sumario: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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