Nature Communications (May 2020)
Inferring multimodal latent topics from electronic health records
- 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
Affiliations
- Yue Li
- School of Computer Science and McGill Centre for Bioinformatics, McGill University
- Pratheeksha Nair
- School of Computer Science and McGill Centre for Bioinformatics, McGill University
- Xing Han Lu
- School of Computer Science and McGill Centre for Bioinformatics, McGill University
- Zhi Wen
- School of Computer Science and McGill Centre for Bioinformatics, McGill University
- Yuening Wang
- School of Computer Science and McGill Centre for Bioinformatics, McGill University
- Amir Ardalan Kalantari Dehaghi
- School of Computer Science and McGill Centre for Bioinformatics, McGill University
- Yan Miao
- School of Computer Science and McGill Centre for Bioinformatics, McGill University
- Weiqi Liu
- School of Computer Science and McGill Centre for Bioinformatics, McGill University
- Tamas Ordog
- Department of Physiology and Biomedical Engineering and Division of Gastroenterology and Hepatology, Department of Medicine, and Center for Individualized Medicine
- Joanna M. Biernacka
- Department of Health Sciences Research, Mayo Clinic
- Euijung Ryu
- Department of Health Sciences Research, Mayo Clinic
- Janet E. Olson
- Department of Health Sciences Research, Mayo Clinic
- Mark A. Frye
- Department of Psychiatry and Psychology, Mayo Clinic
- Aihua Liu
- McGill Adult Unit for Congenital Heart Disease Excellence (MAUDE Unit)
- Liming Guo
- McGill Adult Unit for Congenital Heart Disease Excellence (MAUDE Unit)
- Ariane Marelli
- McGill Adult Unit for Congenital Heart Disease Excellence (MAUDE Unit)
- Yuri Ahuja
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
- Jose Davila-Velderrain
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
- Manolis Kellis
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
- DOI
- https://doi.org/10.1038/s41467-020-16378-3
- Journal volume & issue
-
Vol. 11,
no. 1
pp. 1 – 17
Abstract
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.
