| Summary: | 碩士 === 國立虎尾科技大學 === 資訊管理研究所 === 102 === Through Taiwan’s Health Claim Database, this study investigated the confounding variable combination with the best potential in the big data based on comparative studies on therapy and effectiveness, thereby modifying bias arising form the confounding covariates and achieving the best confounding factor adjustment. In recent years, a number of scholars have pointed out in their research on drug epidemiology that when high-dimensional propensity score-based (hd-PS) approach is applied, through the proper use of rich data in the big database for research and the acquisition of adjusted bias for estimations, the results will be very close to stochastic testing and observational studies, thus deriving at better confounding factor adjustment compared to other methods.
However, how to explore confounding covariates with the highest potential from the big data remains to be a bottleneck faced in past literatures. In the past, the process of selecting confounding covariates using hd-PS involved high reliability on professional knowledge and experiences provided by researchers or literatures.
Through this study, an evolutionary computation approach was proposed to covert the hd-PS method in the Health Claim database with a massive amount of data for exploring important confounding covariates and achieving the optimization of confounding factor adjustment. Hence, a grid computing based evolutionary computation approach was proposed to improve the hd-PS method and obtain the best confounding covariate combination. Findings show that the method put forth in this study was better able to explore the potential confounding variable combination compared to past hd-PS methods, thus achieving the minimization of odds ratio in therapy and efficacy related studies.
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