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This work describes the participation of the University of Texas Health Science Center at Houston (UTHealth) team on the SemEval 2014–Task 7 analysis of clinical text challenge. The task consisted of two subtasks:(1) disorder entity recognition, recognizing mentions of disorder concepts;(2) disorder entity encoding, mapping each mention to a unique Concept Unique Identifier (CUI) defined in Unified Medical Language System (UMLS). We developed three ensemble learning approaches for recognizing disorder entities and a Vector Space Model based method for encoding. Our approaches achieved top rank in both subtasks, with the best F measure of 0.813 for entity recognition and the best accuracy of 74.1% for encoding, indicating the proposed approaches are promising.