We introduce CardioPRIME, a hybrid mechanistic–deep learning framework for electrocardiogram (ECG) analysis, which integrates clinical phenotypes along-side classical ordinary differential equation-based cardiac models. Despite the efficacy of purely data-driven neural networks, real-world medical data are rooted in decades of physiological research. By enforcing consistency between ECG- derived latent representations and multiple clinical modalities, CardioPRIME produces more discriminative and physiologically grounded embeddings than a non- integrated baseline. Our results, reflected by higher clustering metrics (NMI, AMI, Homogeneity, and Completeness) across top prevalent diseases, underscore the significance of clinical integration for improved disease separation and enhanced interpretability. This indicates that bridging physiological modelling with data-driven techniques can substantially advance representation learning in the cardiovascular domain for general disease diagnosis.