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1. A Convolutional Net for Medical Records
Abstract:
Feature engineering remains a major bottleneck when creating predictive systems
from electronic medical records. At present, an important missing element is
detecting predictive regular clinical motifs from irregular episodic records. We
present Deepr (short for Deep record), a new end
that learns to extract features from medical records and predicts future risk
automatically. Deepr transforms a record into a sequence of discrete elements
separated by coded time gaps and hospital
convolutional neural net that detects and combines predictive local clinical motifs
to stratify the risk. Deepr permits transparent inspection and visualization of its
inner working. We validate Deepr on hospital dat
readmission after discharge. Deepr achieves superior accuracy compared to
traditional techniques, detects meaningful clinical motifs, and uncovers the
underlying structure of the disease and intervention space.
A Convolutional Net for Medical Records
Feature engineering remains a major bottleneck when creating predictive systems
from electronic medical records. At present, an important missing element is
detecting predictive regular clinical motifs from irregular episodic records. We
rt for Deep record), a new end-to-end deep learning system
that learns to extract features from medical records and predicts future risk
automatically. Deepr transforms a record into a sequence of discrete elements
separated by coded time gaps and hospital transfers. On top of the sequence is a
convolutional neural net that detects and combines predictive local clinical motifs
to stratify the risk. Deepr permits transparent inspection and visualization of its
inner working. We validate Deepr on hospital data to predict unplanned
readmission after discharge. Deepr achieves superior accuracy compared to
traditional techniques, detects meaningful clinical motifs, and uncovers the
underlying structure of the disease and intervention space.
Feature engineering remains a major bottleneck when creating predictive systems
from electronic medical records. At present, an important missing element is
detecting predictive regular clinical motifs from irregular episodic records. We
end deep learning system
that learns to extract features from medical records and predicts future risk
automatically. Deepr transforms a record into a sequence of discrete elements
transfers. On top of the sequence is a
convolutional neural net that detects and combines predictive local clinical motifs
to stratify the risk. Deepr permits transparent inspection and visualization of its
a to predict unplanned
readmission after discharge. Deepr achieves superior accuracy compared to
traditional techniques, detects meaningful clinical motifs, and uncovers the