We present a study that developed and tested three query expansion methods for the retrieval of clinical documents. Finding relevant documents in a large clinical data warehouse is a challenging task. To address this issue, first, we implemented a synonym expansion strategy that used a few selected vocabularies. Second, we trained a topic model on a large set of clinical documents, which was then used to identify related terms for query expansion. Third, we obtained related terms from a large predicate database derived from Medline abstracts for query expansion. The three expansion methods were tested on a set of clinical notes. All three methods successfully achieved higher average recalls and average F-measures when compared with the baseline method. The average precisions and precision at 10, however, decreased with all expansions. Amongst the three expansion methods, the topic model-based method performed the best in terms of recall and F-measure.