Dilated Convolutional Attention Network for Medical Code Assignment from Clinical Text (2020)

Abstract: Medical code assignment, which predicts medical codes from clinical texts, is a fundamental task of intelligent medical information systems. The emergence of deep models in natural language processing has boosted the development of automatic assignment methods. However, recent advanced neural architectures with flat convolutions or multi-channel feature concatenation ignore the sequential causal constraint within a text sequence and may not learn meaningful clinical text representations, especially for lengthy clinical notes with long-term sequential dependency. This paper proposes a Dilated Convolutional Attention Network (DCAN), integrating dilated convolutions, residual connections, and label attention, for medical code assignment. It adopts dilated convolutions to capture complex medical patterns with a receptive field which increases exponentially with dilation size. Experiments on a real-world clinical dataset empirically show that our model improves the state of the art.

## introduction medical code assignment categorizes clinical documents with sets of codes to facilitate hospital management and improve health record searching @xcite . these clinical texts comprise physiological signals, laboratory tests, and physician notes, where the international classification of diseases (icd) coding system is widely used for annotation. most hospitals rely on manual coding by human coders to assign standard diagnosis codes to the discharge summaries for billing purposes. however, this work is and error-prone @xcite . incorrect coding can cause billing mistakes and mislead other general practitioners when patients are readmitted. intelligent automated coding systems could act as a recommendation system to help coders to allocate correct medical codes to clinical notes. automatic medical code assignment has been intensively researched during the past decades @xcite . recent advances in natural language processing (nlp) with deep learning techniques have inspired many methods for automatic medical code assignment @xcite @xcite . @xcite incorporated structured knowledge into medical text representations by preserving translational property of concept embeddings. however, several challenges remain in medical text understanding. diagnosis notes contain complex diagnosis information, which includes a large number of professional medical vocabulary and noisy information such as non-standard synonyms and misspellings. free text clinical notes are lengthy documents, usually from hundreds to thousands of tokens. thus, medical text understanding requires effective feature representation learning and complex cognitive process to enable multiple diagnosis code assignment. previous neural methods for medical text encoding generally fall into two categories. medical text modeling is commonly regarded as a synonym of recurrent neural networks (rnns) that capture the sequential dependency. such works include atten-tivelstm @xcite , bi-gru @xcite and ha-gru @xcite . the other category uses convolutional neural networks (cnns) such as caml @xcite and multirescnn @xcite . these methods only capture locality but have achieved the optimal predictive performance on medical code assignment. inspired by the generic temporal convolutional network (tcn) architecture @xcite , we consider medical text modeling with causal con-straints, where the encoding of the current token only depends on previous tokens, using the dilated convolutional network. we combine it with the label attention network for fine-grained information aggregation. ## experiments this section introduces the experimental analysis of real-world clinical datasets. our proposed models are compared with several recent strong baselines. the code is publicly available at https://agit . ## conclusion recent years extensively studies the automatic medical code assignment. neural clinical text encoding models use cnns to extract local features and rnns to preserve sequential dependency. this paper combines both by using dilated convolution. the dilated convolutional attention network (dcan) consists of dilated convolution layers, residual connections, and the label attention layer. the dcan model obeys the causal constraint of sequence encoding and learns rich representations to capture label-aware importance. through experiments on the mimic-iii dataset, our model shows better predictive performance than the state-of-theart methods.