Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)
- 1 April 2015
- journal article
- Published by Elsevier in Journal of Biomedical Informatics
- Vol. 54, 96-105
Abstract
No abstract availableKeywords
This publication has 37 references indexed in Scilit:
- Mining high-dimensional administrative claims data to predict early hospital readmissionsJournal of the American Medical Informatics Association, 2014
- Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical DataPLOS ONE, 2013
- Representation Learning: A Review and New PerspectivesIEEE Transactions on Pattern Analysis and Machine Intelligence, 2013
- Next-generation phenotyping of electronic health recordsJournal of the American Medical Informatics Association, 2013
- Mining electronic health records: towards better research applications and clinical careNature Reviews Genetics, 2012
- Temporal abstraction in intelligent clinical data analysis: A surveyArtificial Intelligence in Medicine, 2007
- Reducing the Dimensionality of Data with Neural NetworksScience, 2006
- Training Products of Experts by Minimizing Contrastive DivergenceNeural Computation, 2002
- Comorbidity Measures for Use with Administrative DataMedical Care, 1998
- Using electronic medical records to predict mortality in primary care patients with heart diseaseJournal of General Internal Medicine, 1996