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首页> 外文期刊>Artificial intelligence in medicine >Mining post-surgical care processes in breast cancer patients
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Mining post-surgical care processes in breast cancer patients

机译：在乳腺癌患者中挖掘手术后护理过程

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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.
机译：在这项工作中，我们描述了一种CareFlow挖掘算法的应用，检测3000个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，从乳腺癌诊断后的第一个手术过程中记录。 Careflows从记录的事件数据中挖掘出用于行政用途，包括ICD9 - CM计费代码和化疗治疗的程序。事件数据已预处理主题建模以基于并发过程创建复合事件。 CareFlow采矿算法的结果允许在研究人群中发现电子时间表型。这些表型在临床性状性状和肿瘤组织病理学的基础上进一步表征，以及复发，转移发生和5年的存活率。结果从临床角度来看非常重要，因为表型描述了表征良好的病理学课程，并且Careflows与现有的临床准则很好地匹配。因此，分析促进了可以推出可以通知临床医生以及医院决策者的现实证据。

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