Clinical-Coder: Assigning Interpretable ICD -10 Codes to C hinese Clinical Notes (2020)

Abstract: In this paper, we introduce Clinical-Coder, an online system aiming to assign ICD codes to Chinese clinical notes. ICD coding has been a research hotspot of clinical medicine, but the interpretability of prediction hinders its practical application. We exploit a Dilated Convolutional Attention network with N-gram Matching mechanism (DCANM) to capture semantic features for non-continuous words and continuous n-gram words, concentrating on explaining the reason why each ICD code to be predicted. The experiments demonstrate that our approach is effective and that our system is able to provide supporting information in clinical decision making.

## introduction international classification of disease (icd) is the diagnostic classification standard in the field of clinical medicine, which assigns unique code to each disease. the popularization of icd codes immensely promotes the information sharing and clinical research of disease worldwide and has a positive influence on health condition research, insurance claims, morbidity and mortality statistics @xcite . therefore, icd coding -which assigns proper icd codes to a clinical note -has drawn much attention. it is always that icd coding relies on the manual work of professional staff. the manual coding is very error-prone and time-consuming since the continuous updating version of icd codes results in a substantial increase in code numbers. the number of icd-10 codes is up to 72,184, more than five times the previous version (i.e., icd-9). it allows for more detailed classifications of patients' conditions, injuries, and diseases. however, there * co-first authors, they contributed equally to this work is no doubt that the increased granularity increases the difficulty of manual coding. existing studies came up with several approaches of automatic coding prediction to replace the repetitive manual work, from the traditional machine learning methods @xcite , to neural network methods @xcite . although these methods achieve great success, they are still confronted with a critical challenge, which is the interpretability of predicted codes. explainable model and results are essential for clinical medicine decision making @xcite . thus, the practical approach is supposed to predict correct codes and simultaneously give the reason why each code is predicted. in this paper, we try to provide the interpretability of predictions from a semantic perspective. it is a phenomenon that the exact disease names or similar expressions of disease names often appear in the discharge summary. for example, as shown in figure 1 , the exact matching with disease name such as "fatty liver" is a direct evidence of inference. we call the continuous consistent words as explicit semantic features. moreover, the inexact matching such as "rheumatoid multisite arthritis" is also very useful to predict the codes and should be taken into consideration. we refer to the non-continuous words as implicit semantic features. the two kinds of semantic features are both clues to explain the reason why to assign each code, which is also the basis of experts in manual coding process. to capture the two semantic phenomena, we exploit dilated convolution and n-gram matching mechanism to extract implicit semantic features and explicit semantic features, respectively. furthermore, we develop a system to assist the professional coders in assigning the correct codes. in summary, the main contributions are as follows: it uses vivid visualization to provide interpretability of prediction for each icd code. the site can be accessed by http://159.226. 21.226/disease-prediction , and instructions video is provided at https://youtu. be/u4timtweyse . (for the convenience of readers, the english version is included in the appendix a). the left of figure 2 (a) is the free-text notes user entered, and the right of figure 2 (a) is predicted codes and corresponding disease names. figure 2 (b1) and figure 2 (b2) are the visualization of supporting information for predictions. the detailed description is presented in the section 3.2. ## conclusion in this paper, we propose a dilated convolutional attention network with n-gram matching mechanism (dcanm) for automatic icd coding. the dilated cnn, which is first applied to the icd coding task, aims to capture semantic information for non-continuous words, and the n-gram matching mechanism aims to capture the continuous semantic. they both provide a pretty good interpretability for prediction. moreover, we develop an openaccess system to help users assign icd codes. we will try to utilize external resources to solve the few-shot and zero-shot problem in the future.