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首页> 外文会议>2016 IEEE First Conference on Connected Health: Applications, Systems and Engineering Technologies >TCPM: Topic-Based Clinical Pathway Mining
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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至关重要。但是，由专家设计的数据有限的静态，非自适应临床途径几乎无法在实践中实施。因此，从各种历史数据中挖掘执行临床途径是有意义的。现有工作着重于将过程挖掘或聚类方法应用于医学数据。这些方法通常会产生低粒度过程模型或具有相似处理行为的无序迹线组。在本文中，我们提出了一种基于主题的临床途径挖掘方法，该方法简洁，可解释且具有顺序信息。我们从帐单数据开始，并使用潜在Dirichlet分配对帐单项目进行聚类，而无需指定主题编号。每天的治疗被表示为一组主题，传达了治疗目标。为了强调关键和必要的活动，我们修剪低频主题并删除子迹线。最后，通过对这些主题序列应用模糊挖掘方法，我们可以发现执行临床路径。在真实数据集上的实验表明了我们方法的有效性和实用性。

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