Ledieu Thibault, Bouzille Guillaume, Plaisant Catherine, Thiessard Frantz, Polard Elisabeth, Cuggia Marc
INSERM, UMR 1099, Rennes, F-35000, France.
Université de Rennes 1, LTSI, Rennes, F-35000, France.
AMIA Annu Symp Proc. 2018 Dec 5;2018:1368-1376. eCollection 2018.
Health data mining can bring valuable information for drug safety activities. We developed a visual analytics tool to find specific clinical event sequences within the data contained in a clinical data warehouse. To this aim, we adapted the Smith-Waterman DNA sequence alignment algorithm to retrieve clinical event sequences with a temporal pattern from the electronic health records included in a clinical data warehouse. A web interface facilitates interactive query specification and result visualization. We describe the adaptation of the Smith-Waterman algorithm, and the implemented user interface. The evaluation with pharmacovigilance use cases involved the detection of inadequate treatment decisions in patient sequences. The precision and recall results (F-measure = 0.87) suggest that our adaptation of the Smith-Waterman-based algorithm is well-suited for this type of pharmacovigilance activities. The user interface allowed the rapid identification of cases of inadequate treatment.
健康数据挖掘可为药物安全活动带来有价值的信息。我们开发了一种可视化分析工具，以在临床数据仓库中包含的数据内查找特定的临床事件序列。为此，我们改编了史密斯-沃特曼DNA序列比对算法，以从临床数据仓库中包含的电子健康记录中检索具有时间模式的临床事件序列。一个网页界面便于进行交互式查询指定和结果可视化。我们描述了史密斯-沃特曼算法的改编以及所实现的用户界面。用药警戒用例的评估涉及检测患者序列中不充分的治疗决策。精确率和召回率结果（F值=0.87）表明，我们基于史密斯-沃特曼算法的改编非常适合此类用药警戒活动。用户界面能够快速识别治疗不充分的病例。
