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The rapid evolution of artificial intelligence in healthcare necessitates robust infrastructures capable of integrating advanced computational models into clinical workflows. This conceptual manuscript proposes a transformer-embedded clinical phenotyping infrastructure model, designed to enhance the extraction and utilization of patient phenotypes from electronic health records (EHRs) through transformer-based architectures. By embedding transformer mechanisms within a multi-layered infrastructure, the model facilitates dynamic phenotyping, enabling precise patient stratification and decision support without relying on empirical data or performance metrics. The framework emphasizes interoperability, governance, and seamless integration with existing healthcare analytics ecosystems, addressing challenges in data exchange and AI deployment. Key components include a phenotypic encoding layer, a transformer orchestration module, and a feedback loop for continuous refinement. Conceptual formulas are introduced to interpret risk propagation in phenotyping errors, decision confidence in clinical outputs, monitoring burdens on system resources, resource allocation for computational efficiency, governance loads in regulatory compliance, and sensitivity to data drift. This model contributes to theoretical discussions on AI-driven healthcare systems by outlining an architecture that prioritizes ethical deployment and clinical utility. Through literature synthesis, it draws on recent advancements in clinical AI architectures and EHR intelligence, positioning the infrastructure as a foundational element for future intelligent health systems. The implications extend to improved clinical phenotyping accuracy and infrastructure resilience in diverse healthcare settings.

Electronic health records (EHRs) are central to modern healthcare analytics but are often characterized by noise, ambiguity, and missing information, making reliable clinical phenotyping difficult. Clinical phenotypes—observable characteristics derived from patient data—are essential for diagnosis, prognosis, and treatment planning. Yet, traditional supervised machine learning methods depend on large volumes of high-quality annotated data that are difficult to obtain at scale.This review examines the role of weak supervision in enabling scalable clinical phenotyping from noisy and heterogeneous EHR data. Weak supervision frameworks generate labels using heuristic rules, knowledge-based signals, or programmatic labeling functions, allowing models to learn from large datasets without extensive expert annotation. These approaches help address challenges such as inconsistent terminology, missing values, and temporal irregularities commonly found in clinical records.We synthesize recent developments in scalable phenotyping systems that integrate machine learning architectures, probabilistic labeling strategies, and multimodal data representations to extract meaningful patterns from imperfect clinical data. The review also outlines a systems-level perspective on healthcare analytics pipelines, covering data ingestion, model training under label uncertainty, deployment in clinical environments, and governance considerations for responsible AI integration.Overall, weak supervision emerges as a practical strategy for transforming noisy EHR data into usable clinical intelligence, enabling more scalable and trustworthy analytics for healthcare decision support.

Long COVID (post-acute sequelae of SARS-CoV-2 infection, PASC) affects roughly 10–30% of COVID-19 survivors and is marked by persistent symptoms such as fatigue, cognitive dysfunction (“brain fog”), shortness of breath, loss of smell, and post-exertional malaise that can last for months or years, while its underlying biological mechanisms and validated diagnostic biomarkers remain unclear. The condition is highly heterogeneous, with patients showing different recovery patterns and no clearly defined clinical subtypes, and the scarcity of labeled datasets further limits the use of supervised machine learning methods for phenotyping. To address this, we propose a self-supervised contrastive multi-view learning framework that integrates three temporal data modalities—pre-infection electronic health records, acute-phase clinical and biomarker data (e.g., CRP, ferritin, D-dimer, lymphocyte counts), and post-acute symptom trajectories—using separate encoders and a shared latent space aligned through contrastive learning without requiring phenotype labels, followed by unsupervised clustering to identify potential subtypes. By exploiting the natural temporal linkage within each patient and contrasts across patients, this approach enables data-driven discovery of long COVID phenotypes, supports early prediction of subgroup membership, and may ultimately inform personalized treatment strategies, clinical trial design, and improved understanding of disease mechanisms.