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

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  • Qing Yang
  • Ting Luo
  • Wei Zhang
  • Xiaorong Zhong
  • Ping He
  • Hong Zheng

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

Suggested Citation

  • Qing Yang & Ting Luo & Wei Zhang & Xiaorong Zhong & Ping He & Hong Zheng, 2022. "Data‐driven treatment pathways mining for early breast cancer using cSPADE algorithm and system clustering," International Journal of Health Planning and Management, Wiley Blackwell, vol. 37(5), pages 2569-2584, September.
    • Handle: RePEc:bla:ijhplm:v:37:y:2022:i:5:p:2569-2584
    DOI: 10.1002/hpm.3483
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