Effects of Mining Parameters on the Performance of the Sequence Pattern Variants Analyzing Method Applied to Electronic Medical Record Systems

Sequential pattern mining (SPM) is widely used for data mining and knowledge discovery in various application domains. Recently, we have proposed an analyzing method to evaluate the sequence pattern variant (SPV) that is the original sequence containing frequent patterns including variants. Such a study is meaningful for medical tasks such as improving the quality of a disease's treatment method. This paper aims to evaluate the effectiveness of the proposed analyzing method in more detail when it was applied to Electronic Medical Record Systems. Using a real dataset, it is observed that the analyzing method is successful in statistically discovering the meaningful indicators that are leading to the difference between comparative SPVs, such as complicated risk, severity risk of the disease, the length of stay in the hospital and the total medical cost. Moreover, it is observed that the length of stay and the medical cost can gain more benefit from increasing the significance level parameter used in comparing the SPVs.