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. 2022 Sep;37(5):2569-2584.
doi: 10.1002/hpm.3483. Epub 2022 Apr 20.

Data-driven treatment pathways mining for early breast cancer using cSPADE algorithm and system clustering

Qing Yang  1 , Ting Luo  2 , Wei Zhang  3 , Xiaorong Zhong  2   4 , Ping He  2 , Hong Zheng  2   4
Affiliations

Data-driven treatment pathways mining for early breast cancer using cSPADE algorithm and system clustering

Qing Yang et al. Int J Health Plann Manage. 2022 Sep.

Abstract

Objectives: Due to the multidimensional, multilayered, and chronological order of the cancer data, it was challenging for us to extract treatment paths. 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, three 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: We have proved that cSPADE algorithm combined system clustering is an effective technique for identifying temporal relationships between treatment modalities, enabling hierarchical and vertical mining of breast cancer treatment models. In addition, we confirmed the robustness of the results by cross-validation of these treatment pathway ordering rules. Through this method, the treatment path of early breast cancer patients can be revealed, and the real-world breast cancer treatment behaviour model can be evaluated, which can provide reference for the redesign and optimization of treatment path.

Keywords: breast cancer; clinical pathway; cluster analysis; data mining; sequential pattern mining.

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References

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