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Impulse Technologies
                                      Beacons U to World of technology
        044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in
        A Method for Mining Infrequent Causal Associations and Its
        Application in Finding Adverse Drug Reaction Signal Pairs
   Abstract
          Discovering infrequent causal relationships can help us prevent or correct
   negative outcomes caused by their antecedents. In this paper, we propose an
   innovative data mining framework and apply it to mine potential causal
   associations in electronic patient datasets where the drug-related events of interest
   occur infrequently. Specifically, we created a novel interestingness measure,
   exclusive causal-leverage, based on a computational, fuzzy recognition-primed
   decision (RPD) model that we previously developed. On the basis of this new
   measure, a data mining algorithm was developed to mine the causal relationship
   between drugs and their associated adverse drug reactions (ADRs). The exclusive
   causal-leverage was employed to rank the potential causal associations between
   each of the three selected drugs (i.e., enalapril, pravastatin and rosuvastatin) and
   3,954 recorded symptoms, each of which corresponded to a potential ADR. The
   top 10 drug-symptom pairs for each drug were evaluated by the physicians on our
   project team. The numbers of symptoms considered as likely real ADRs for
   enalapril, pravastatin and rosuvastatin were 8, 7, and 6, respectively. These
   preliminary results indicate the usefulness of our method in finding potential ADR
   signal pairs for further analysis (e.g., epidemiology study) and investigation (e.g.,
   case review) by drug safety professionals.




  Your Own Ideas or Any project from any company can be Implemented
at Better price (All Projects can be done in Java or DotNet whichever the student wants)
                                                                                           1

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8

  • 1. Impulse Technologies Beacons U to World of technology 044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in A Method for Mining Infrequent Causal Associations and Its Application in Finding Adverse Drug Reaction Signal Pairs Abstract Discovering infrequent causal relationships can help us prevent or correct negative outcomes caused by their antecedents. In this paper, we propose an innovative data mining framework and apply it to mine potential causal associations in electronic patient datasets where the drug-related events of interest occur infrequently. Specifically, we created a novel interestingness measure, exclusive causal-leverage, based on a computational, fuzzy recognition-primed decision (RPD) model that we previously developed. On the basis of this new measure, a data mining algorithm was developed to mine the causal relationship between drugs and their associated adverse drug reactions (ADRs). The exclusive causal-leverage was employed to rank the potential causal associations between each of the three selected drugs (i.e., enalapril, pravastatin and rosuvastatin) and 3,954 recorded symptoms, each of which corresponded to a potential ADR. The top 10 drug-symptom pairs for each drug were evaluated by the physicians on our project team. The numbers of symptoms considered as likely real ADRs for enalapril, pravastatin and rosuvastatin were 8, 7, and 6, respectively. These preliminary results indicate the usefulness of our method in finding potential ADR signal pairs for further analysis (e.g., epidemiology study) and investigation (e.g., case review) by drug safety professionals. Your Own Ideas or Any project from any company can be Implemented at Better price (All Projects can be done in Java or DotNet whichever the student wants) 1