The use of sequential pattern mining to predict next prescribed medications
The use of sequential pattern mining to predict next prescribed medications
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DOI：
10.1016/j.jbi.2014.09.003
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发表时间：
2015-02-01
影响因子：
4.5
通讯作者：
Sittig, Dean F.
中科院分区：
文献类型：
--
作者：
Wright, Aileen P.;Wright, Adam T.;Sittig, Dean F.
背景资料：某些疾病的治疗是逐步进行的，其中一种药物被推荐为初始治疗，其他药物随后使用。序列模式挖掘是一种数据挖掘技术，用于识别有序事件的模式。目的：确定序列模式挖掘是否能有效识别药物之间的时间关系，并准确预测患者可能处方的下一种药物。设计：我们从德克萨斯州的蓝十字蓝盾获得了2008年至2011年期间至少开过一种糖尿病药物的患者的索赔数据，并将其分为训练集（90%的患者）和测试集（10%的患者）。我们应用CSPADE算法在药物类别和仿制药水平上挖掘糖尿病药物处方的序列模式，并通过支持统计量对其进行排名。然后，我们评估了预测的准确性，其中糖尿病药物的患者可能是prescribed.Results：我们确定了161,497例患者已被规定至少一种糖尿病药物。我们能够挖掘出与指南一致的药物治疗的逐步模式。在三次尝试中，当按药物类别进行预测时，我们能够预测90.0%的患者的处方药物，当在仿制药水平进行预测时，我们能够预测64.1%的患者的处方药物。这些结果在10倍交叉验证下是稳定的，在药物类别水平范围为89.1%-90.5%，在仿制药水平范围为63.5%-64.9%。使用1或2个项目在病人的用药史导致更准确的预测比不使用任何历史，但使用整个历史有时world.Conclusion：序列模式挖掘是一种有效的技术，以确定药物之间的时间关系，并可用于预测下一步在病人的用药方案。准确的预测可以在不使用患者的整个用药史的情况下进行。(C)2014 Elsevier Inc. All rights reserved.
Background: Therapy for certain medical conditions occurs in a stepwise fashion, where one medication is recommended as initial therapy and other medications follow. Sequential pattern mining is a data mining technique used to identify patterns of ordered events.Objective: To determine whether sequential pattern mining is effective for identifying temporal relationships between medications and accurately predicting the next medication likely to be prescribed for a patient.Design: We obtained claims data from Blue Cross Blue Shield of Texas for patients prescribed at least one diabetes medication between 2008 and 2011, and divided these into a training set (90% of patients) and test set (10% of patients). We applied the CSPADE algorithm to mine sequential patterns of diabetes medication prescriptions both at the drug class and generic drug level and ranked them by the support statistic. We then evaluated the accuracy of predictions made for which diabetes medication a patient was likely to be prescribed next.Results: We identified 161,497 patients who had been prescribed at least one diabetes medication. We were able to mine stepwise patterns of pharmacological therapy that were consistent with guidelines. Within three attempts, we were able to predict the medication prescribed for 90.0% of patients when making predictions by drug class, and for 64.1% when making predictions at the generic drug level. These results were stable under 10-fold cross validation, ranging from 89.1%-90.5% at the drug class level and 63.5-64.9% at the generic drug level. Using 1 or 2 items in the patient's medication history led to more accurate predictions than not using any history, but using the entire history was sometimes worse.Conclusion: Sequential pattern mining is an effective technique to identify temporal relationships between medications and can be used to predict next steps in a patient's medication regimen. Accurate predictions can be made without using the patient's entire medication history. (C) 2014 Elsevier Inc. All rights reserved.