The automatic discovery of a model to represent the history of encounters of a group of patients with the healthcare system -- the so-called "pathway of patients" -- is a new field of research that supports clinical and organisational decisions to improve the quality and efficiency of the treatment provided. The pathways of patients with chronic conditions tend to vary significantly from one person to another, have repetitive tasks, and demand the analysis of multiple perspectives (interventions, diagnoses, medical specialities, among others) influencing the results. Therefore, modelling and mining those pathways is still a challenging task. In this work, we propose a framework comprising: (i) a pathway model based on a multi-aspect graph, (ii) a novel dissimilarity measurement to compare pathways taking the elapsed time into account, and (iii) a mining method based on traditional centrality measures to discover the most relevant steps of the pathways. We evaluated the framework using the study cases of pregnancy and diabetes, which revealed its usefulness in finding clusters of similar pathways, representing them in an easy-to-interpret way, and highlighting the most significant patterns according to multiple perspectives.

翻译：自动发现能够代表一组患者与医疗系统接触历史的模型——即所谓的“患者路径”——是一个新的研究领域，它支持临床和组织决策，以提升所提供治疗的质量与效率。慢性病患者的路径往往因人而异，具有重复性任务，并需要分析影响结果的多重视角（如干预措施、诊断、医学专科等）。因此，建模和挖掘这些路径仍然是一项具有挑战性的任务。在本工作中，我们提出了一个框架，包括：（i）基于多维度图的路径模型，（ii）一种考虑经过时间的新型相异性度量方法以比较路径，以及（iii）一种基于传统中心性度量的挖掘方法，用于发现路径中最相关的步骤。我们通过妊娠和糖尿病的案例研究对该框架进行了评估，结果表明其在发现相似路径聚类、以易于理解的方式表示这些路径以及突出多重视角下的最重要模式方面具有实用价值。