  The text of clinical notes can be a valuable source of patient information
and clinical assessments. Historically, the primary approach for exploiting
clinical notes has been information extraction: linking spans of text to
concepts in a detailed domain ontology. However, recent work has demonstrated
the potential of supervised machine learning to extract document-level codes
directly from the raw text of clinical notes. We propose to bridge the gap
between the two approaches with two novel syntheses: (1) treating extracted
concepts as features, which are used to supplement or replace the text of the
note; (2) treating extracted concepts as labels, which are used to learn a
better representation of the text. Unfortunately, the resulting concepts do not
yield performance gains on the document-level clinical coding task. We explore
possible explanations and future research directions.
