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

Development of deep learning algorithms to categorize free-text notes  pertaining to diabetes: convolution neural networks achieve higher accuracy  than support vector machines