MIMIC-IV-Ext-CEKG: A Process-Oriented Dataset Derived from MIMIC-IV for Enhanced Clinical Insights

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.