LAUSR

In China, people have been using traditional Chinese medicine (TCM) to treat diseases for thousands of years. Doctors combine patient symptoms with TCM theory to devise formulae composed of several traditional medicines. With the rapid development of the Internet of Things (IoT), the Internet of Medical Things (IoMT) is gaining popularity in the TCM domain. Consequently, a large number of TCM formulae with different therapeutic effects have been accumulated in IoMT. Therefore, we presented PWFP, which is an efficient methodology for extracting the top-K weighted frequent patterns for IoMT. PWFP guarantees efficient mining performance by estimating the minimum weighted support threshold value. Furthermore, PWFP can be applied in the efficacy-based analysis of these TCM formulae. This study conducted several experiments on both general datasets and TCM formulae for glomerulonephritis stored in IoMT. The rankings of the patterns mined from the prescriptions displayed a good rise in clinical efficacy. Doctors can use these top-k effective patterns as new drug and medicine combination candidates when they conduct drug discovery research and perform clinical medicine selection.