human disease network guided discovery of interesting itemsets in hospital discharge data
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.
human disease networks association rules itemset discovery