  Predicting patient mortality is an important and challenging problem in the
healthcare domain, especially for intensive care unit (ICU) patients.
Electronic health notes serve as a rich source for learning patient
representations, that can facilitate effective risk assessment. However, a
large portion of clinical notes are unstructured and also contain domain
specific terminologies, from which we need to extract structured information.
In this paper, we introduce an embedding framework to learn
semantically-plausible distributed representations of clinical notes that
exploits the semantic correspondence between the unstructured texts and their
corresponding structured knowledge, known as semantic frame, in a hierarchical
fashion. Our approach integrates text modeling and semantic correspondence
learning into a single model that comprises 1) an unstructured embedding module
that makes use of self-similarity matrix representations in order to inject
structural regularities of different segments inherent in clinical texts to
promote local coherence, 2) a structured embedding module to embed the semantic
frames (e.g., UMLS semantic types) with deep ConvNet and 3) a hierarchical
semantic correspondence module that embeds by enhancing the interactions
between text-semantic frame embedding pairs at multiple levels (i.e., words,
sentence, note). Evaluations on multiple embedding benchmarks on post discharge
intensive care patient mortality prediction tasks demonstrate its effectiveness
compared to approaches that do not exploit the semantic interactions between
structured and unstructured information present in clinical notes.
