  Postsurgical complications (PSCs) are known as a deviation from the normal
postsurgical course and categorized by severity and treatment requirements.
Surgical site infection (SSI) is one of major PSCs and the most common
healthcare-associated infection, resulting in increased length of hospital stay
and cost. In this work, we assessed an automated way to generate lexicon (i.e.,
keyword features) from clinical narratives using sublanguage analysis with
heuristics to detect SSI and evaluated these keywords with medical experts. To
further validate our approach, we also conducted decision tree algorithm on
cohort using automatically generated keywords. The results show that our
framework was able to identify SSI keywords from clinical narratives and to
support search-based natural language processing (NLP) approaches by augmenting
search queries.
