Improving the future of healthcare starts by better understanding the current actual practices in hospital settings. This motivates the objective of discovering typical care pathways from patient data. Revealing typical care pathways can be achieved through clustering. The difficulty in clustering care pathways, represented by sequences of timestamped events, lies in defining a semantically appropriate metric and clustering algorithms. In this article, we adapt two methods developed for time series to the clustering of timed sequences: the drop-DTW metric and the DBA approach for the construction of averaged time sequences. These methods are then applied in clustering algorithms to propose original and sound clustering algorithms for timed sequences. This approach is experimented with and evaluated on synthetic and real-world data.

翻译：提升医疗保健的未来始于更好地理解医院环境中当前的实际实践。这促使了从患者数据中发现典型诊疗路径的目标。通过聚类可以揭示典型的诊疗路径。对以带时间戳事件序列表示的诊疗路径进行聚类的难点，在于定义语义上合适的度量标准和聚类算法。在本文中，我们将两种为时间序列开发的方法适配到时序序列的聚类中：用于时序序列的drop-DTW度量标准，以及用于构建平均时间序列的DBA方法。随后将这些方法应用于聚类算法中，为时序序列提出了原创且可靠的聚类算法。该方法在合成数据和真实数据上进行了实验与评估。