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We developed and evaluated a UMLS Metathesaurus Co-occurrence mining algorithm to connect medications and diseases they may treat. Based on 16 years of co-occurrence data, we created 977 candidate drug-disease pairs for a sample of 100 ingredients (50 commonly prescribed and 50 selected at random). Our evaluation showed that more than 80% of the candidate drug-disease pairs were rated "APPROPRIATE" by physician raters. Additionally, there was a highly significant correlation between the overall frequency of citation and the likelihood that the connection was rated "APPROPRIATE." The drug-disease pairs were used to initialize term definitions in an ongoing effort to build a medication reference terminology for the Veterans Health Administration. Co-occurrence mining is a valuable technique for initializing term definitions in a large-scale reference terminology creation project.