Table_1_A Process Mining Pipeline to Characterize COVID-19 Patients' Trajectories and Identify Relevant Temporal Phenotypes From EHR Data.DOCX

The impact of the COVID-19 pandemic involved the disruption of the processes of care and the need for immediately effective re-organizational procedures. In the context of digital health, it is of paramount importance to determine how a specific patients' population reflects into the healthcare dynamics of the hospital, to investigate how patients' sub-group/strata respond to the different care processes, in order to generate novel hypotheses regarding the most effective healthcare strategies. We present an analysis pipeline based on the heterogeneous collected data aimed at identifying the most frequent healthcare processes patterns, jointly analyzing them with demographic and physiological disease trajectories, and stratify the observed cohort on the basis of the mined patterns. This is a process-oriented pipeline which integrates process mining algorithms, and trajectory mining by topological data analyses and pseudo time approaches. Data was collected for 1,179 COVID-19 positive patients, hospitalized at the Italian Hospital “Istituti Clinici Salvatore Maugeri” in Lombardy, integrating different sources including text admission letters, EHR and hospital infrastructure data. We identified five temporal phenotypes, from laboratory values trajectories, which are characterized by statistically significant different death risk estimates. The process mining algorithms allowed splitting the data in sub-cohorts as function of the pandemic waves and of the temporal trajectories showing statistically significant differences in terms of events characteristics.

新冠疫情的爆发对医疗护理流程造成了严重干扰，并迫切需要实施即时有效的重组措施。在数字健康领域，确定特定患者群体如何反映到医院医疗动态中，探究患者亚组/层次如何对不同护理流程作出响应，以生成关于最有效医疗策略的全新假设，显得尤为关键。本研究提出了一种基于异构收集数据的分析流程，旨在识别最频繁的医疗护理流程模式，并将这些模式与人口统计学和生理疾病轨迹进行联合分析，并根据挖掘出的模式对观察到的队列进行分层。这是一个以流程为导向的流程，它集成了流程挖掘算法、拓扑数据分析以及伪时间方法进行的轨迹挖掘。数据收集自1,179名确诊为COVID-19的患者，他们被收容于位于伦巴第的意大利医院“Salvatore Maugeri Istituti Clinici”。数据来源包括入院信函、电子健康记录以及医院基础设施数据。我们根据实验室值轨迹识别出五种时间表型，这些表型在死亡风险估计方面具有统计学上的显著差异。流程挖掘算法允许根据疫情波次和时间轨迹将数据分割成亚队列，这些亚队列在事件特征方面显示出统计学上的显著差异。