Automatic Diagnosis Coding of Radiology Reports: A Comparison of Deep Learning and Conventional Classification Methods

Abstract

Diagnosis autocoding is intended to both improve the productivity of clinical coders and the accuracy of the coding. We investigate the applicability of deep learning at autocoding of radiology reports using International Classification of Diseases (ICD). Deep learning methods are known to require large training data. Our goal is to explore how to use these methods when the training data is sparse, skewed and relatively small, and how their effectiveness compares to conventional methods. We identify optimal parameters for setting up a convolutional neural network for autocoding with comparable results to that of conventional methods.