Knowledge guided multi-filter residual convolutional neural network for ICD coding from clinical text

Abstract A common challenge encountered when using Deep Neural Network models for automatic ICD coding is their potential inability to effectively handle unseen clinical texts, especially these are only trained on a limited number of examples. This because rely solely the patterns and relationships present in training data, may not be able incorporate additional knowledge about between medical entities. To address this issue, we introduce KG-MultiResCNN— K nowledge G uided Multi -filter Res idual C onvolutional N eural etwork model, which combines examples with external from Wikidata Knowledge Graph (KG) order better capture The KG structured database that contains wealth information various entities, including concepts one another. By incorporating into our improve its ability predict codes new texts. In experiments MIMIC-III dataset, found KG-MultiResCNN model significantly outperformed baseline approaches. demonstrates effectiveness knowledge, addition examples, performance deep learning coding.