Learning vector representation of medical objects via EMR-driven nonnegative restricted Boltzmann machines (eNRBM)
Electronic medical record (EMR) offers promises for novel analytics. However, manual feature engineering from EMR is labor intensive because EMR is complex – it contains temporal, mixed-type and multimodal data packed in irregular episodes. We present a computational framework to harness EMR with mi...
| Main Authors: | , , , |
|---|---|
| Format: | Journal Article |
| Published: |
Academic Press Inc.
2015
|
| Online Access: | http://hdl.handle.net/20.500.11937/23215 |