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A ubiquitous task in processing electronic medical data is the assignment ofstandardized codes representing diagnoses and/or procedures to free-textdocuments such as medical reports. This is a difficult natural languageprocessing task that requires parsing long, heterogeneous documents andselecting a set of appropriate codes from tens of thousands ofpossibilities---many of which have very few positive training samples. Wepresent a deep learning system that advances the state of the art for theMIMIC-III dataset, achieving a new best micro F1-measure of 55.85\%,significantly outperforming the previous best result (Mullenbach et al. 2018).We achieve this through a number of enhancements, including two major novelcontributions: multi-view convolutional channels, which effectively learn toadjust kernel sizes throughout the input; and attention regularization,mediated by natural-language code descriptions, which helps overcome sparsityfor thousands of uncommon codes. These and other modifications are selected toaddress difficulties inherent to both automated coding specifically and deeplearning generally. Finally, we investigate our accuracy results in detail toindividually measure the impact of these contributions and point the waytowards future algorithmic improvements.

Medical code prediction with multi-view convolution and description-regularized label-dependent attention