Analysis of Transitions in Differences between Frequent Medical-order Sequences for COVID-19

With the increasing use of electronic medical records, medical support from analysis of the accumulated medical information is expected. Currently, new treatment methods and drugs are being developed for the treatment of new diseases, but the transition history of medical orders has yet to be visualized for diseases such as COVID-19. In this paper, we use sequential pattern mining to extract frequent medical orders and then apply the longest common subsequence variant (LCSV) and merged sequence variant (MSV) to analyze the differences in treatment patterns at different times. We also propose three types of sliding window (time interval window, sequence number window, and time-sequence number window) to analyze the transition history of medical orders. As an example, we applied these methods to Japanese electronic medical records covering the first to the fifth waves of COVID-19 and analyzed the differences in medical-order patterns for the five infection waves and the transition history of medical orders. We then visualized the difference with MSV. The results showed that the proposed method can successfully visualize the differences in medical orders between infection waves, and the transition history of medical orders can be revealed. The validity of the results was confirmed by the medical staff involved.