Patient Journey Modeling Through Multi-Modal Even Fusion for Early ICD Coding

The International Classification of Diseases (ICD) system standardizes diagnostic and procedural coding, yet manual code assignment remains labor-intensive and error-prone. Most automated ICD coding approaches rely on discharge summaries, limiting their applicability to early-inpatient coding. Recent sequential models mitigate this by processing the longitudinal sequence of clinical notes recorded throughout hospitalization, but they often overlook structured clinical events or integrate them without precise temporal alignment. We propose a Note-Oriented Multi-modal Alignment (NOMA) model that enriches clinical note representations with temporally aligned structured events, including laboratory tests, drug administrations, and microbiology results. The patient journey is modeled as a chronologically ordered sequence in which each note can access only events that occurred earlier in time. This design preserves temporal causality, prevents information leakage, and enables reliable early prediction when clinical information is still sparse. Experiments on the MIMIC-III dataset show that the proposed approach outperforms strong baselines based on discharge summaries and sequential note modeling, particularly in early and intermediate prediction scenarios. The results demonstrate the value of temporally consistent multi-modal fusion for early ICD coding. Code and pre-processed are available here https: //github. com/Peterzazy/PatientJourneyModelling. git.