  Automated ICD coding, which assigns the International Classification of
Disease codes to patient visits, has attracted much research attention since it
can save time and labor for billing. The previous state-of-the-art model
utilized one convolutional layer to build document representations for
predicting ICD codes. However, the lengths and grammar of text fragments, which
are closely related to ICD coding, vary a lot in different documents.
Therefore, a flat and fixed-length convolutional architecture may not be
capable of learning good document representations. In this paper, we proposed a
Multi-Filter Residual Convolutional Neural Network (MultiResCNN) for ICD
coding. The innovations of our model are two-folds: it utilizes a multi-filter
convolutional layer to capture various text patterns with different lengths and
a residual convolutional layer to enlarge the receptive field. We evaluated the
effectiveness of our model on the widely-used MIMIC dataset. On the full code
set of MIMIC-III, our model outperformed the state-of-the-art model in 4 out of
6 evaluation metrics. On the top-50 code set of MIMIC-III and the full code set
of MIMIC-II, our model outperformed all the existing and state-of-the-art
models in all evaluation metrics. The code is available at
https://github.com/foxlf823/Multi-Filter-Residual-Convolutional-Neural-Network.
