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

2018/09/16 by Boyi Yang, Yang, Boyi, Adam Wright +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Health Professions · Mathematics · #Artificial Intelligence in Healthcare #Biomedical Text Mining and Ontologies #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #cs.CL #cs.LG #stat.ML

paper · pdf · doi:10.48550/arxiv.1809.05814

9 pages, 4 figures, submitted to Journal of the American Medical Informatics Association (JAMIA) on September 15th, 2018

arxiv created 2018/09/16 · openalex publication_date 2018/09/16 · arxiv updated 2018/09/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention
• Convolutional Neural Networks for Sentence Classification

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