Identification of postsurgical complications is the first step towards improving patient safety and health care quality as well as reducing heath care cost. Existing NLP-based approaches for retrieving postsurgical complications are based on search strategies. Here, we conduct a sublanguage analysis study using free text reports available for a cohort of patients with postsurgical complications identified manually to compare the keywords identified by subject matter experts with words/phrases automatically identified by sublanguage analysis. The results suggest that search-based approaches may miss some cases and the sublanguage analysis results can be used as a base to develop an information extraction system or support search-based NLP approaches by augmenting search queries.