# A Comparison of Deep Learning Methods for ICD Coding of Clinical Records
###### Abstract  
In this survey, we discuss the task of automatically classifying medical documents into the taxonomy of the International Classification of Diseases (ICD), by the use of deep neural networks. The literature in this domain covers different techniques. We will assess and compare the performance of those techniques in various settings and investigate which combination leverages the best results. Furthermore, we introduce an hierarchical component that exploits the knowledge of the ICD taxonomy. All methods and their combinations are evaluated on two publicly available datasets that represent ICD-9 and ICD-10 coding, respectively. The evaluation leads to a discussion of the advantages and disadvantages of the models. Keywords:ICD coding; hierarchical classification; electronic healthcare +
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displayed in the upper part of Figure 1. In the lower part of this figure, a concrete example of the coding is shown. In this paper, we survey state-of-the-art deep learning approaches for ICD-9 coding. We especially focus on the representation learning that the methods accomplish. Experiments with ICD-9 are carried out on the MIMIC-III dataset [2]. This dataset consists of over 50,000 discharge summaries of patient visits in US hospitals. These summaries are in free textual format and labeled with corresponding ICD-9 codes, an example snippet is visible in Figure 2. Most discharge summaries are labeled with multiple categories, leading to a multiclass and multilabel setting for category prediction. Figure 1: ICD-9 code structure with second through fourth layer representations and diabetes as an example. Figure 2: Example snippet of discharge summary from the MIMIC-III dataset with corresponding target International Classification of Diseases (ICD) codes. Codes from the ICD-10 version are very similar to those of ICD-9. The main difference is that they consist of up to seven characters of which at least the first three are always present, the latter four are optional. The first character is an uppercase alphabetic letter, all other characters are numeric. The first three characters indicate the category of the diagnoses, and the following three characters indicate the etiology, anatomic site, severity, or other clinical details. A seventh character indicates an extension. An example of the ICD-10 structure is visible in Figure 3, it visualizes the same diagnosis as in Figure 1 but for ICD-10 instead of ICD-9. Experiments with ICD-10 are conducted on the CodiEsp dataset, which is publicly available. This dataset consists of 1000 discharge summaries of patient visits in Spain. The documents are in free text format, which is automatically translated to English from Spanish, and they are manually labeled with ICD-10 codes by healthcare professionals. The deep learning methods that we discuss in this paper encompass different neural network architectures including convolutional and recurrent neural networks. It is studied how they can be extended with suitable attention mechanisms and loss functions and how the hierarchical structure of the ICD taxonomy can be exploited. ICD-10 coding is especially challenging, as in the benchmark dataset that we use for our experiments the ICD coding model has to deal with very few manually labeled training data. In our work we want to answer the following research questions. What are the current state-of-the-art neural network approaches for classifying discharge summaries? How do they compare to each other in terms of performance? What combination of techniques gives the best results on a public dataset? We hypothesize the following claims. (1) A combination of self-attention and convolutional layers yields the best classification results. (2) In a setting with less training samples per category, attention on description vectors of the target categories improves the results. (3) Using the hierarchical taxonomy explicitly in the model improves classification on a small dataset.