Temporal and comorbidity-aware representation of longitudinal patient trajectories from electronic health records

Electronic health records (EHRs) capture longitudinal multi-visit patient journeys but are difficult to analyze due to temporal irregularity, multimorbidity, and heterogeneous coding. This study introduces a temporal and comorbidity-aware trajectory representation that restructures admissions into ordered symbolic visit states while preserving diagnostic progression, secondary comorbidities, procedure categories, demographics, outcomes, and inter-visit intervals. These symbolic states are subsequently encoded as fixed-length numerical vectors suitable for computational analysis. Validation was conducted in two stages: Stage I assessed construction fidelity using coverage metrics, comorbidity preservation, diagnostic transition structures, and exact inter-visit gap encoding and Stage II assessed analytical utility through clustering experiments using different clustering approacheslike sequence similarity, Gaussian Mixture Models (GMM), and a temporal LSTM autoencoder (TS-LSTM). Proof of concept was done by encoding subset of patient cohorts from the MIMIC-IV database consisting of 2,280 patients with 8,849 admissions having complete primary diagnosis coverage and near-complete secondary coverage. Stage 1 assessment consisting of cohort-level coverage metrics confirmed that the transformation preserved essential clinical information and key properties of longitudinal EHRs. In Stage 2, clustering experiments validated the analytical utility of the representation across sequence-based, Gaussian mixture, and temporal LSTM autoencoder approaches. Ablation studies further demonstrated that both multimorbidity depth and inter-visit gap encoding are critical to maintaining cluster separability and temporal fidelity. The findings show that explicit encoding of comorbidity and timing improves interpretability and subgroup coherence. Although evaluated on a single dataset, the use of standardised ICD-10 EHR structure supports the assumption that the framework can generalise across healthcare settings; future work will incorporate multimodal data and external validation.