  Health professionals can use natural language processing (NLP) technologies
when reviewing electronic health records (EHR). Machine learning free-text
classifiers can help them identify problems and make critical decisions. We aim
to develop deep learning neural network algorithms that identify EHR progress
notes pertaining to diabetes and validate the algorithms at two institutions.
The data used are 2,000 EHR progress notes retrieved from patients with
diabetes and all notes were annotated manually as diabetic or non-diabetic.
Several deep learning classifiers were developed, and their performances were
evaluated with the area under the ROC curve (AUC). The convolutional neural
network (CNN) model with a separable convolution layer accurately identified
diabetes-related notes in the Brigham and Womens Hospital testing set with the
highest AUC of 0.975. Deep learning classifiers can be used to identify EHR
progress notes pertaining to diabetes. In particular, the CNN-based classifier
can achieve a higher AUC than an SVM-based classifier.
