Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining the most interesting rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Standard knowledge discovery techniques, such as unsupervised or supervised descriptive rule discovery, have been widely used in medical data mining. Most of the research is focused on developing effective association rule evaluation metrics that would allow discovery of exceptional and interesting patterns. This study tries to integrate information on comorbidity obtained from recently very popular human disease networks, to rule learning from large medical datasets. The proposed approach is presented in a novel application of age related itemset mining from hospital discharge data. Such approach allows discovery of emerging patterns based on the age of patients that can be used to identify the age groups with the increased risk of comorbidities.