A Hybrid Pooling Based Deep Learning Framework For Automated ICD Coding

ICD coding is the practice of allocating diagnostic and procedure codes to the clinical records following the International Classification of Diseases. The manual allocation of ICD codes to clinical notes is a very tedious job which has become costly, time-consuming and error-prone. Up to now, various methods for automated ICD coding have been devised, ranging from machine learning to deep learning methodologies. Earlier cutting-edge models relied on CNN’s with one or several fixed window widths. However, the length and dependency of text fragments linked to ICD labels in clinical literature differ considerably, posing a difficulty in determining the optimal window size. Apart from that, in prior models that utilized CNN architecture, the average features of the clinical notes have been ignored, resulting in a lack of preparation of rich features for the classifier. In this research, we present a Deep Recurrent Convolutional Neural Network with Hybrid Pooling (DRCNN-HP), which addresses all of the above mentioned issues. DRCNN-HP takes into account the different lengths as well as the dependency of the ICD code-related text chunks. Furthermore, we applied a powerful hybrid pooling layer in DRCNN-HP to capture the rich feature representation (i.e., by concatenating maximum and average features of text) for the classifier, which resulted in giving state-of-the-art results on the MIMIC-III top 50 dataset as compared to the prior competitive models.