Medical coding or classification is the process of transforming information contained in patient medical records into standard predefined medical codes. There are several worldwide accepted medical coding conventions associated with diagnoses and medical procedures; however, in the United States the Ninth Revision of ICD(ICD-9) provides the standard for coding clinical records. Accurate medical coding is important since it is used by hospitals for insurance billing purposes. Since after discharge a patient can be assigned or classified to several ICD-9 codes, the coding problem can be seen as a multi-label classification problem. In this paper, we introduce a multi-label large-margin classifier that automatically learns the underlying inter-code structure and allows the controlled incorporation of prior knowledge about medical code relationships. In addition to refining and learning the code relationships, our classifier can also utilize this shared information to improve its performance. Experiments on a publicly available dataset containing clinical free text and their associated medical codes showed that our proposed multi-label classifier outperforms related multi-label models in this problem.