Automatic ICD code assignment utilizing textual descriptions and hierarchical structure of ICD code

International Classification of Diseases (ICD) codes provides a hierarchy of diagnostic codes for classifying diseases, which plays an important role in healthcare. Medical coding which assigns a subset of ICD codes to a patient visit is a mandatory process that is crucial for patient care and billing. Manual coding is time-consuming, expensive, and error-prone. In this paper, we present a deep learning based network that utilizes the information of textual descriptions and hierarchical structure of ICD codes to make a code assignment automatically. On one hand, we use the machine comprehension framework to model the relationship between the diagnostic description and the textual descriptions, enabling the model put more attention on most relevant segments of diagnostic description for each possible codes. On the other hand, we exploit tree-LSTM architecture to model hierarchical structure, capturing the hierarchical relationship among codes and the semantics of each code. We demonstrate the effectiveness of the proposed methods on a Chinese dataset and an English dataset.