Process mining is a suitable method for knowledge extraction from patient pathways. Structured in event logs, medical events are complex, often described using various medical codes. An efficient labeling of these events before applying process mining analysis is challenging. This paper presents an innovative methodology to handle the complexity of events in medical event logs. Based on autoencoding, accurate labels are created by clustering similar events in latent space. Moreover, the explanation of created labels is provided by the decoding of its corresponding events. Tested on synthetic events, the method is able to find hidden clusters on sparse binary data, as well as accurately explain created labels. A case study on real healthcare data is performed. Results confirm the suitability of the method to extract knowledge from complex event logs representing patient pathways.

中文翻译：

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使用自动编码对医疗事件日志进行自动和可解释的标记。

过程挖掘是一种从患者路径中提取知识的合适方法。在事件日志中，医疗事件很复杂，通常使用各种医疗代码进行描述。在应用流程挖掘分析之前对这些事件进行有效标记具有挑战性。本文提出了一种处理医疗事件日志中事件复杂性的创新方法。基于自动编码，通过在潜在空间中聚类相似事件来创建准确的标签。此外，对创建的标签的解释是通过对其相应事件的解码提供的。在合成事件上进行测试，该方法能够在稀疏二进制数据上找到隐藏的集群，并准确解释创建的标签。对真实医疗保健数据进行了案例研究。