Clinical Pathways and Digital Twins in MIMIC-IV: A Proof-of-Concept Study

This study examines whether MIMIC-IV can support the construction of clinical pathways and proof-of-concept digital twins, and whether such pathways can be updated automatically with machine learning. Using time-stamped hospital records, patient-level event logs were reconstructed from admissions, diagnoses, procedures, and selected derived variables. Directed graphs were used to visualize trajectories, and a baseline machine-learning pipeline regenerated predictive features when new events were added. MIMIC-IV proved suitable for pathway reconstruction because it contains enough temporal detail to order events and link pathway states to outcomes. However, the pathway structure was heterogeneous, no single universal pathway was adequate, and the results must be interpreted in the context of a US-based database and a proof-of-concept study design rather than as directly generalizable clinical recommendations.