MIMIC-IV-Ext-CEKG: A Process-Oriented Dataset Derived from MIMIC-IV for Enhanced Clinical Insights
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Maintaining a healthy population is essential for improving quality of life and overall societal well-being. One approach to achieving a healthy population is by improving patients' care pathways. This is particularly vital for patients with multiple chronic conditions, who require well-coordinated care across various medical specialties. One approach to improving and analyzing the care pathways of these types of patients is process mining. The clinical event knowledge graph is a recent framework in process mining introduced for patients with multimorbidity that facilitates standardized interpretation of care pathways by linking to ICD-10 and SNOMED-CT. It also facilitates storing recent multi-entity event data in the event graph and analyzing care pathways for multimorbid patients from multiple perspectives. MIMIC-IV is a dataset that facilitates data analysis in healthcare; however, it is not specialized for process mining and requires extensive data preprocessing to prepare it for process mining. This paper contributes to the MIMIC-IV-Ext-CEKG dataset, an extracted dataset from MIMIC-IV that facilitates using the Clinical Event Knowledge Graph framework and other process mining tasks. This paper describes its characteristics and how it is extracted from the MIMIC-IV dataset. MIMIC-IV-Ext-CEKG facilitates deploying MIMIC-IV for process mining.
维持健康的人口群体对于提升生活质量与整体社会福祉至关重要。改善患者的照护路径是实现健康人口的可行路径之一，这对于需要跨多医学专科开展协同照护的多种慢性病患者而言尤为关键。流程挖掘（process mining）则是改善并分析这类患者照护路径的有效方法之一。临床事件知识图谱（Clinical Event Knowledge Graph）是近年来流程挖掘领域针对共病患者推出的新型框架，通过关联ICD-10与SNOMED-CT术语集，可实现照护路径的标准化解读。该框架还支持在事件图谱中存储多实体的最新事件数据，并可从多维度分析共病患者的照护路径。MIMIC-IV是一款助力医疗数据分析的数据集，但并未针对流程挖掘做专门优化，需经过大量数据预处理才能适配流程挖掘任务。本文构建了MIMIC-IV-Ext-CEKG数据集，该数据集从MIMIC-IV中提取而来，可便捷支持临床事件知识图谱框架的应用及其他流程挖掘任务。本文详述了该数据集的特性，以及其从MIMIC-IV数据集中提取的具体方法。MIMIC-IV-Ext-CEKG可推动MIMIC-IV在流程挖掘领域的落地应用。
