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We present a semantically interpretable system for automated ICD coding of clinical text documents. Our contribution is an ontological attention mechanism which matches the structure of the ICD ontology, in which shared attention vectors are learned at each level of the hierarchy, and combined into label-dependent ensembles. Analysis of the attention heads shows that shared concepts are learned by the lowest common denominator node. This allows child nodes to focus on the differentiatingdoi:10.18653/v1/d19-6220 dblp:conf/acl-louhi/FalisPLSDMTO19 fatcat:w6gk5fyhfrhhppkxgxegq5jaia