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Named entity recognition (NER) is an important task for natural language processing (NLP) of clinical text. Conditional Random Fields (CRFs), a sequential labeling algorithm, and Support Vector Machines (SVMs), which is based on large margin theory, are two typical machine learning algorithms that have been widely applied to NER tasks, including clinical entity recognition. However, Structural Support Vector Machines (SSVMs), an algorithm that combines the advantages of both CRFs and SVMs, has not been investigated for clinical text processing. In this study, we applied the SSVMs algorithm to the Concept Extraction task of the 2010 i2b2 clinical NLP challenge, which was to recognize entities of medical problems, treatments, and tests from hospital discharge summaries. Using the same training (N = 27,837) and test (N = 45,009) sets in the challenge, our evaluation showed that the SSVMs-based NER …