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TCPM: Topic-Based Clinical Pathway Mining

机译:TCPM:基于主题的临床途径挖掘

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摘要

Clinical pathway is important for improving medical quality, reducing cost and regulating resource. However, a static, non-adaptive clinical pathway designed by experts with limited data can be hardly implemented in practice. Thus, mining the execution clinical pathway from various historical data is meaningful. Existing works focus on applying either process mining or clustering methods on medical data. These methods generally produce low-granularity process models or unordered trace groups with similar treatment behaviors. In this paper, we propose a topic-based clinical pathway mining approach, which is concise, interpretable and of sequential information. We start from billing data, and use Latent Dirichlet Allocation to cluster billing items without specifying the topic number. The treatment of each day is represented as a set of topics, which convey the treatment goals. To emphasize critical and essential activities, we prune the low-frequency topics and remove sub-traces. Finally, by applying fuzzy mining method on these topic sequences, we can discover the execution clinical pathway. The experiments on a real-world data set show the effectiveness and practicability of our approach.
机译:临床途径对于提高医疗质量,降低成本和调节资源非常重要。然而,在实践中可以很难实施由具有有限数据的专家设计的静态非自适应临床途径。因此,从各种历史数据中挖掘执行临床途径是有意义的。现有的工作侧重于在医疗数据上应用流程挖掘或聚类方法。这些方法通常产生具有相似治疗行为的低粒度过程模型或无序的痕量组。在本文中,我们提出了一种基于主题的临床途径挖掘方法,其简明扼要,可解释和顺序信息。我们从账单数据开始,并使用潜在的Dirichlet分配到群集计费项目而不指定主题号。每天的治疗都是作为一系列主题,传达治疗目标。为了强调关键和基本的活动,我们会修剪低频主题并删除子迹线。最后,通过在这些主题序列上应用模糊挖掘方法,我们可以发现执行临床途径。实际数据集的实验表明了我们方法的有效性和实用性。

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