In the medical domain, user-generated social media text is increasingly used as a valuable complementary knowledge source to scientific medical literature: it contains the unprompted experiences of the patient. Yet, lexical normalization of such data has not been addressed properly. This paper presents a sequential, unsupervised pipeline for automatic lexical normalization of domain-specific abbreviations and spelling mistakes. This pipeline led to an absolute reduction of out-of-vocabulary terms of 0.82% and 0.78% in two cancer-related forums. Our approach mainly targeted, and thus corrected, medical concepts. Consequently, our pipeline may significantly improve downstream IR tasks.