With the non-stop increases in medical treatment fees, dealing with the high financial costs has become a burden for the Bureau of National Health Insurance (BNHI) of Taiwan. Thus, with the objective of reducing expenses, it introduced in 2007 the Diagnosis Related Group, a system that classifies medical cases into groups based on the International Classification of Diseases (ICD), Ninth Revision, Clinical Modification. The economic survival of a hospital relies on the reimbursements received from the BNHI, which in turn depend on the accuracy and completeness of the content of the discharge summaries as well as the correctness of their ICD codes, so it is essential to improve the quality of coding of disease classification specialists. This study develops an ICD code advisory system that performs knowledge discovery from discharge summaries with the objective of providing a means of knowledge representation and supporting disease classification specialists in the coding process. Natural language processing and information retrieval techniques based on Zipf’s Law are applied to process the content of discharge summaries, and fuzzy formal concept analysis is used to analyze and represent the relationships between the medical terms identified. The resulting advisory system analyzes discharge summaries and suggests ICD codes supported by a certainty factor that can be used as reference during the coding process.

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