Methods for Analyzing Medical-Order Sequence Variants in Sequential Pattern Mining for Electronic Medical Record Systems

Abstract

Electronic medical record systems have been adopted by many large hospitals worldwide, enabling the recorded data to be analyzed by various computer-based techniques to gain a better understanding of hospital-based disease treatments. Among such techniques, sequential pattern mining, already widely used for data mining and knowledge discovery in other application domains, has shown great potential for discovering frequent patterns in sequences of disease treatments. However, studies have yet to evaluate the use of medical-order sequence variants, where a “frequent pattern” can include some limited variations to the pattern, or have considered the factors that lead to these variants. Such a study would be meaningful for medical tasks such as improving the quality of a particular treatment method, comparing treatments with multiple hospitals, recommending the best-suited treatment for each patient, and optimizing the running costs in hospitals. This article proposes methods for evaluating medical-order sequence variants and understanding variant factors based on a statistical approach. We consider the safety and efficiency of sequences and related information about the variants, such as gender, age, and test results from hospitals. Our proposal has been demonstrated as effective by experimentally evaluating an electronic medical record system’s real dataset and obtaining feedback from medical workers. The experimental results indicate that the medical treatment history and specimen test results after hospitalization are significant in identifying the factors that lead to variants.