Pharmacy Department, 55647Hospital European Georges Pompidou, Paris, FR.
Clinical Research Department, 55647Hospital European Georges Pompidou, Paris, FR.
Health Informatics J. 2022 Apr-Jun;28(2):14604582221101526. doi: 10.1177/14604582221101526.
We evaluated the ability of a coupled pattern-mining and clustering method to identify homogeneous groups of subjects in terms of healthcare resource use, prognosis and treatment sequences, in renal cancer patients beginning oral anticancer treatment.
Data were retrieved from the permanent sample of the French medico-administrative database. We applied the CP-SPAM algorithm for pattern mining to healthcare use sequences, followed by hierarchical clustering on principal components (HCPC).
We identified 127 individuals with renal cancer with a first reimbursement of an oral anticancer drug between 2010 and 2017. Clustering identified three groups of subjects, and discrimination between these groups was good. These clusters differed significantly in terms of mortality at six and 12 months, and medical follow-up profile (predominantly outpatient or inpatient care, biological monitoring, reimbursement of supportive care drugs). This case study highlights the potential utility of applying sequence-mining algorithms to a large range of healthcare reimbursement data, to identify groups of subjects homogeneous in terms of their care pathways and medical behaviors.
我们评估了一种联合模式挖掘和聚类方法，以识别接受口服抗癌治疗的肾癌患者在医疗资源利用、预后和治疗序列方面具有同质特征的患者群体。
从法国医疗管理数据库的永久样本中提取数据。我们应用 CP-SPAM 算法对医疗使用序列进行模式挖掘，然后对主成分进行层次聚类（HCPC）。
我们确定了 2010 年至 2017 年间首次报销口服抗癌药物的 127 名肾癌患者。聚类识别出了 3 组患者，这些组之间的区分度良好。这些组在 6 个月和 12 个月的死亡率以及医疗随访情况（主要是门诊或住院护理、生物监测、支持性护理药物报销）方面存在显著差异。该案例研究强调了应用序列挖掘算法分析大量医疗报销数据，以识别在治疗途径和医疗行为方面具有同质特征的患者群体的潜在效用。
