The International Classification of Diseases ( ICD ) provides a standardized way for classifying diseases , which endows each disease with a unique code . ICD coding aims to assign proper ICD codes to a medical record . Since manual coding is very laborious and prone to errors , many methods have been proposed for the automatic ICD coding task . However , most of existing methods independently predict each code , ignoring two important characteristics : Code Hierarchy and Code Co-occurrence . In this paper , we propose a Hyperbolic and Co-graph Representation method ( HyperCore ) to address the above problem . Specifically , we propose a hyperbolic representation method to leverage the code hierarchy . Moreover , we propose a graph convolutional network to utilize the code co-occurrence . Experimental results on two widely used datasets demonstrate that our proposed model outperforms previous state-ofthe-art methods . The International Classification of Diseases ( ICD ) is a healthcare classification system supported by the World Health Organization , which provides a unique code for each disease , symptom , sign and so on . ICD codes have been widely used for analyzing clinical data and monitoring health issues ( Choi et al . , 2016 ; Avati et al . , 2018 ) . Due to the importance of ICD codes , ICD coding -which assigns proper ICD codes to a medical record -has drawn much attention . The task of ICD coding is usually undertaken by professional coders according to doctors ' diagnosis descriptions in the form of free texts . However , manual coding is very expensive , time-consuming and error-prone . The cost incurred by coding errors and the financial investment spent on improving coding quality are estimated to be $ 25 billion per year in the US ( Lang , 2007 ) . Two main reasons can account for this . First , only the people who have medical expert knowledge and specialized ICD coding skills can handle the task . However , it is hard to train such an eligible ICD coder . Second , it is difficult to correctly assign proper codes to the input document even for professional coders , because one document can be assigned multiple ICD codes and the number of codes in the taxonomy of ICD is large . For example , there are over 15,000 and 60,000 codes respectively in the ninth version ( ICD-9 ) and the tenth version ( ICD-10 ) of ICD taxonomies . To reduce human labor and coding errors , many methods have been carefully designed for automatic ICD coding ( Perotte et al . , 2013 ; Mullenbach et al . , 2018 ) . For example in Figure 1 , given the clinical text of a patient , the ICD coding model needs to automatically predict the corresponding ICD codes . The automatic ICD coding task can be modeled as a multi-label classification task since each clinical text is usually accompanied by mul- tiple codes . Most of the previous methods handle each code in isolation and convert the multi-label problem into a set of binary classification problems to predict whether each code of interest presents or not ( Mullenbach et al . , 2018 ; Rios and Kavuluru , 2018 ) . Though effective , they ignore two important characteristics : Code Hierarchy and Code Co-occurrence , which can be leveraged to improve coding accuracy . In the following , we will introduce the two characteristics and the reasons why they are critical for the automatic ICD coding . Code Hierarchy : Based on ICD taxonomy , ICD codes are organized under a tree-like hierarchical structure as shown in Figure 2 , which indicates the parent-child and sibling relations between codes . In the hierarchical structure , the upper level nodes represent more generic disease categories and the lower level nodes represent more specific diseases . The code hierarchy can capture the mutual exclusion of some codes . If code X and Y are both children of Z ( i.e. , X and Y are the siblings ) , it is unlikely to simultaneously assign X and Y to a patient in general ( Xie and Xing , 2018 ) . For example in Figure 2 , if code " 464.00 ( acute laryngitis without mention of obstruction ) " is assigned to a patient , it is unlikely to assign the code " 464.01 ( acute laryngitis with obstruction ) " to the patient at the same time . If automatic ICD coding models ignore such a characteristic , they are prone to giving inconsistent predictions . Thus , a challenging problem is how to model the code hierarchy and use it to capture the mutual exclusion of codes . Code Co-occurrence : Since some diseases are concurrent or have a causal relationship with each other , their codes usually co-occur in the clinical text , such as " 997.91 ( hypertension ) " and " 429.9 ( heart disease ) " . In this paper , we call such characteristic code co-occurrence which can capture the correlations of codes . The code co-occurrence can be utilized to correctly predict some codes which are difficult to predict by only using the clinical text itself . For example in Figure 1 , the code of " acute respiratory failure " can be easily inferred via capturing apparent clues ( i.e. , the green bold words ) from the text . Although there are also a few clues to infer the code of " acidosis " , they are very obscure , let alone predict the code of " acidosis " by only using these obscure clues . Fortunately , there is a strong association between these two diseases : one of the main causes of " acidosis " is " acute respiratory failure " . This prior knowledge can be captured via the fact that the codes of the two diseases usually co-occur in clinical texts . By considering the correlation , the automatic ICD coding model can better exploit obscure clues to predict the code of " acidosis " . Therefore , another problem is how to leverage code co-occurrence for ICD coding . In this paper , we propose a novel method termed as Hyperbolic and Co-graph Representation method ( HyperCore ) to address above problems . Since the tree-likeness properties of the hyperbolic space make it more suitable for representing symbolic data with hierarchical structures than the Euclidean space ( Nickel and Kiela , 2017 ) , we propose a hyperbolic representation learning method to learn the Code Hierarchy . Meanwhile , the graph has been proved effective in modeling data correlation and the graph convolutional network ( GCN ) enables to efficiently learn node representation ( Kipf and Welling , 2016 ) . Thus , we devise a code co-occurrence graph ( co-graph ) for capturing Code Co-occurrence and exploit the GCN to learn the code representation in the co-graph . The contributions of this paper are threefold . Firstly , to our best knowledge , this is the first work to propose a hyperbolic representation method to leverage the code hierarchy for automatic ICD coding . Secondly , this is also the first work to utilize a GCN to exploit code co-occurrence correlation for automatic ICD coding . Thirdly , experiments on two widely used automatic ICD coding datasets show that our proposed model outperforms previous state-of-the-art methods . In this paper , we propose a novel hyperbolic and cograph representation framework for the automatic ICD coding task , which can jointly exploit code hierarchy and code co-occurrence . We exploit the hyperbolic representation learning method to leverage the code hierarchy in the hyperbolic space . Moreover , we use the graph convolutional network to capture the co-occurrence correlation . Experimental results on two widely used datasets indicate that our proposed model outperforms previous state-ofthe-art methods . We believe our method can also be applied to other tasks that need to exploit hierarchical label structure and label co-occurrence , such as fine-grained entity typing and hierarchical multi-label classification .
Existing models that classify texts in medical records into the International Classification of Diseases reduce manual efforts however they ignore Code Hierarchy and Code Co-occurrence.
They propose a hyperbolic representation method to leverage the code hierarchy and a graph convolutional network to utilize the code-occurrence for automatic coding.
The proposed model outperforms state-of-the-art methods on two widely used datasets.