Data-Driven Treatment Pathways Mining for Early Breast Cancer Using cSPADE Algorithm and System Clustering

Abstract Background: Due to the multidimensional, multilayered, and chronological order of the cancer data in this study, it was challenging for us to extract treatment paths. Therefore, it was necessary to design a new data mining scheme to effectively extract the treatment path of breast cancer. To determine whether the cSPADE algorithm and system clustering proposed in this study can effectively identify the treatment pathways for early breast cancer. Methods: We applied data mining technology to the electronic medical records of 6891 early breast cancer patients to mine treatment pathways. We provided a method of extracting data from EMR and performed three-stage mining: determining the treatment stage through the cSPADE algorithm → system clustering for treatment plan extraction → cSPADE mining sequence pattern for treatment. The Kolmogorov-Smirnov test and correlation analysis were used to cross-validate the sequence rules of early breast cancer treatment pathways.Results: We unearthed 55 sequence rules for early breast cancer treatment, 3 preoperative neoadjuvant chemotherapy regimens, 3 postoperative chemotherapy regimens, and 2 chemotherapy regimens for patients without surgery. Through 5-fold cross-validation, Pearson and Spearman correlation tests were performed. At the significance level of P <0.05, all correlation coefficients of support, confidence and lift were greater than 0.89. Using the Kolmogorov-Smirnov test, we found no significant differences between the sequence distributions.Conclusions: The cSPADE algorithm combined with system clustering can achieve hierarchical and vertical mining of breast cancer treatment models. By uncovering the treatment pathways of early breast cancer patients by this method, the real-world breast cancer treatment behavior model can be evaluated, and it can provide a reference for the redesign and optimization of the treatment pathways.