基于优化主题模型的临床路径挖掘

在健康领域，诊疗过程对于医疗质量至关重要.临床路径集合了各种医疗知识，是对诊疗过程进行标准化的重要途径.然而，当前大多数临床路径由专家研讨制定，往往静态不变，难以部署和实施.在我们之前的工作中，提出了一种基于主题的临床路径挖掘算法，可以从医疗数据中抽取历史执行路径，客观反映数据中实际存在的医疗模式.算法首先通过主题模型将繁杂的诊疗活动聚合成若干主题，而每个诊疗日就可以表示为一个主题分布，一个病人的诊疗日志也相应的转换为一个主题序列，然后利用过程挖掘方法从这些主题序列中生成基于主题的临床路径模型.但传统主题模型（LDA）的聚类效果往往难以满足医疗数据的特点，导致主题质量不高，影响最终过程模型的可解释性.其中，一个普遍的问题就是LDA无法保证两个相似的诊疗日所得的主题分布也是相似的，这是由于其忽略了诊疗日之间原有的相似性特征.在本文中，我们提出了一种优化的主题模型算法，该算法引入了基于本体生成的诊疗日相似性约束，可以有效提升聚类效果.实验结果表明，我们提出的方法能够发现更符合医疗领域特点的高质量主题，进而为基于主题的临床路径的挖掘奠定基础.

Optimized Topic Model for Clinical Pathway Mining

In healthcare domain, the care process is critical for the care quality. Clinical Pathway (CP), which integrates a lot of medical knowledge, is a tool for standardizing the care process. However, most of existing CPs are designed by experts with limited experience and data, so that they are always static and non-adaptive for implementing. According to our previous work, topic-based CP mining is an effective approach which can discover the process model from clinical data. The various clinical activities are summarized into several topics by Latent Dirichlet Allocation (LDA), and each clinical day in the patient trace is converted to a topic distribution. A CP model can be derived by applying process mining method on the topic-based sequences. However, LDA ignores the similarity between clinical days, which means that in some cases, two similar days may be assigned quite different topic distributions. In this paper, we proposed an optimized topic modeling for clinical topic discovering by incorporating the similarity constraint, which is based on the domain knowledge. Experiments on real data demonstrate that our approach can discover quality topics, which are useful for topic-based CP mining.