  International Classification of Diseases (ICD) are the de facto codes used
globally for clinical coding. These codes enable healthcare providers to claim
reimbursement and facilitate efficient storage and retrieval of diagnostic
information. The problem of automatically assigning ICD codes has been
approached in literature as a multilabel classification, using neural models on
unstructured data. Our proposed approach enhances the performance of neural
models by effectively training word vectors using routine medical data as well
as external knowledge from scientific articles. Furthermore, we exploit the
geometric properties of the two sets of word vectors and combine them into a
common dimensional space, using meta-embedding techniques. We demonstrate the
efficacy of this approach for a multimodal setting, using unstructured and
structured information. We empirically show that our approach improves the
current state-of-the-art deep learning architectures and benefits ensemble
models.
