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Application of Hyper-geometric Hypothesis-based Quanti cation and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection

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2012, MS, University of Cincinnati, Engineering and Applied Science: Mechanical Engineering.
Pharmacovigilance is the science relating to all concerns about drug safety, especially of managing the risk associated with medications. It serves as a complementary approach to clinical trial. Spontaneous Reporting Systems (SRS) had been constructed world-widely to facilitate tracking the risk of post-marketing drugs. Data mining algorithms had been used for years in detecting possible adverse eects of drugs by analyzing the large amount of data in SRS. This study consists of two parts. One is to propose a statistically sound bivariate analysis method. The objective is to provide a method with a sound theoretical base and an ability of conguring itself for dierent demanded performances. The bivariate analysis method pro- posed in the study is termed Hyper-geometric Hypothesis-based method. This new method is inspired by statistical acceptance sampling techniques used in quality control. It is proposed as an alternative to conventional disproportionality analysis methods such as reporting odds ratio (ROR) and proportional reporting ratio (PRR). The second is to investigate the eec- tiveness of a feature selection approach to reduce false alarms through the identication of confounding drugs. Confounding drug is one of the major sources for false signals generated by established methods. The feature selection method is based on the concept of a Markov blanket that removes features that do not have unique contribution to distinguishing the target concept. It is proposed as an alternative to the emerging Bayesian logistic regression method for detecting adverse drug reaction. Experiments have been conducted using the Adverse Event Reporting System (AERS) main- tained by the US Food and Drug Administration. The results showed that the performance of the Hyper-geometric Hypothesis based quantication method was comparable to that of ROR and PRR by adopting the threshold, P-value = 0.0409, which had been trained through the experiment data. The feature selection approach was able to partially detect confound- ing drugs in the meantime it left a number of dangerous drugs not alarmed. In contrast, Bayesian logistic regression method fails to live up to returning any results to make alarms on drugs.
Hongdao Huang, PhD (Committee Chair)
Alex Lin, PhD (Committee Member)
David Thompson, PhD (Committee Member)
77 p.

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Citations

  • Zhang, Y. (2012). Application of Hyper-geometric Hypothesis-based Quanti cation and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669

    APA Style (7th edition)

  • Zhang, Yi. Application of Hyper-geometric Hypothesis-based Quanti cation and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection. 2012. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669.

    MLA Style (8th edition)

  • Zhang, Yi. "Application of Hyper-geometric Hypothesis-based Quanti cation and Markov Blanket Feature Selection Methods to Generate Signals for Adverse Drug Reaction Detection." Master's thesis, University of Cincinnati, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353343669

    Chicago Manual of Style (17th edition)