Developing Transformer Based Clinical Decision Support Systems for Early Detection and Risk Stratification of Cardiovascular Diseases in Real World Healthcare Environments

Abstract

The integration of Transformer-based architectures into clinical decision support systems represents a paradigm shift in the early detection and risk stratification of cardiovascular diseases. While traditional predictive models have relied on static clinical variables and linear regression techniques, the inherent complexity of longitudinal electronic health records requires more sophisticated attention-based mechanisms capable of capturing temporal dependencies and cross-modal correlations. This research explores the development, architectural design, and systemic deployment of large-scale Transformer models tailored for real-world healthcare environments. We examine the structural trade-offs between model complexity and clinical interpretability, particularly focusing on how self-attention mechanisms can be leveraged to identify subtle physiological precursors to heart failure, stroke, and myocardial infarction within heterogeneous data streams. Beyond technical performance, the paper provides a deep analytical discussion on the socio-technical infrastructure required to sustain these systems, including the governance of data pipelines, the robustness of model inference under varying clinical conditions, and the ethical implications of algorithmic fairness in underserved populations. By situating the technical discussion within the broader context of healthcare policy and institutional sustainability, this work highlights the necessary transition from experimental pilot projects to resilient, production-grade clinical infrastructures. We conclude that while Transformers offer unprecedented predictive power, their long-term clinical utility depends on the seamless alignment of algorithmic innovation with institutional governance and human-centric design.