In this work we describe the application of a careflow mining algorithm to detect the most frequent patterns of care in a cohort of 3000 breast cancer patients. The applied method relies on longitudinal data extracted from electronic health records, recorded from the first surgical procedure after a breast cancer diagnosis. Careflows are mined from events data recorded for administrative purposes, including procedures from ICD9 – CM billing codes and chemotherapy treatments. Events data have been pre-processed with Topic Modelling to create composite events based on concurrent procedures. The results of the careflow mining algorithm allow the discovery of electronic temporal phenotypes across the studied population. These phenotypes are further characterized on the basis of clinical traits and tumour histopathology, as well as in terms of relapses, metastasis occurrence and 5-year survival rates. Results are highly significant from a clinical perspective, since phenotypes describe well characterized pathology classes, and the careflows are well matched with existing clinical guidelines. The analysis thus facilitates deriving real-world evidence that can inform clinicians as well as hospital decision makers.

在这项工作中，我们描述了应用护理流程挖掘算法来检测 3000 名乳腺癌患者队列中最常见的护理模式。所应用的方法依赖于从电子健康记录中提取的纵向数据，这些数据是从乳腺癌诊断后的第一次手术过程中记录的。Careflows 是从出于管理目的记录的事件数据中挖掘出来的，包括来自 ICD9 – CM 计费代码和化疗治疗的程序。事件数据已使用主题建模进行预处理，以创建基于并发程序的复合事件。护理流挖掘算法的结果允许在所研究的人群中发现电子时间表型。这些表型根据临床特征和肿瘤组织病理学以及复发情况进一步表征，转移发生率和 5 年生存率。从临床角度来看，结果非常重要，因为表型描述了充分表征的病理类别，并且护理流程与现有的临床指南非常匹配。因此，该分析有助于推导出真实世界的证据，为临床医生和医院决策者提供信息。