{"id":{"repo_id":"umkc","oai_identifier":"oai:mospace.umsystem.edu:10355/45647"},"canonical_url":"https://search.dev.ndltd.org/etd/umkc/oai:mospace.umsystem.edu:10355/45647","repository":{"repo_id":"umkc","name":"University of Missouri - Kansas City","base_url":"https://mospace.umsystem.edu/oai/request"},"display":{"title":"Identifying relationships among drug side-effects using probabilistic association rule mining","abstract":"Side-effects of medical drugs have important implications for health care. Knowledge of side-effects can help guide appropriate prescription of drugs to patients. The study of drug side-effects is also a rich area of informatics research, for example linking side-effects to genetic variation or finding new uses for drugs by comparative analysis of side-effects. This thesis contributes to the latter area of research by proposing a method to determine associations between side-effects. The problem is cast in the form of a merged basket analysis problem. A modified version of association rule mining together with the use of hierarchical terminology is employed to rank potential associations between side-effects. Results are validated by comparison with conventional association rule mining under different assumptions of uncertainty in a real world dataset. The proposed approach is general enough to be applicable to a range of problems where observations are probabilistic.","abstract_html":"Side-effects of medical drugs have important implications for health care. Knowledge of side-effects can help guide appropriate prescription of drugs to patients. The study of drug side-effects is also a rich area of informatics research, for example linking side-effects to genetic variation or finding new uses for drugs by comparative analysis of side-effects. This thesis contributes to the latter area of research by proposing a method to determine associations between side-effects. The problem is cast in the form of a merged basket analysis problem. A modified version of association rule mining together with the use of hierarchical terminology is employed to rank potential associations between side-effects. Results are validated by comparison with conventional association rule mining under different assumptions of uncertainty in a real world dataset. 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The study of drug side-effects is also a rich area of informatics research, for example linking side-effects to genetic variation or finding new uses for drugs by comparative analysis of side-effects. This thesis contributes to the latter area of research by proposing a method to determine associations between side-effects. The problem is cast in the form of a merged basket analysis problem. A modified version of association rule mining together with the use of hierarchical terminology is employed to rank potential associations between side-effects. Results are validated by comparison with conventional association rule mining under different assumptions of uncertainty in a real world dataset. 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