  We present an automatic mortality prediction scheme based on the unstructured
textual content of clinical notes. Proposing a convolutional document embedding
approach, our empirical investigation using the MIMIC-III intensive care
database shows significant performance gains compared to previously employed
methods such as latent topic distributions or generic doc2vec embeddings. These
improvements are especially pronounced for the difficult problem of
post-discharge mortality prediction.
