International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
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A REVIEW OF INTELLIGENT DEEP LEARNING FRAMEWORK FOR EARLY-STAGE CLINICAL RISK IDENTIFICATION USING LONGITUDINAL PATIENT RECORDS Shivangi Singh1, Mr. Manish Kumar Soni2 1Master of Technology, Computer Science and Engineering, Bansal Institute of Engineering & Technology,
Lucknow, India
2Assistant Professor, Department of Computer Science and Engineering, Bansal Institute of Engineering &
Technology, Lucknow, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - Early-stage clinical risk identification plays a
traditional statistical models in several clinical domains (Esteva et al., 2019; Miotto et al., 2018). This section establishes the motivation, conceptual foundations, and scope of the present review.
critical role in preventive healthcare by enabling timely intervention and reducing morbidity, mortality, and treatment costs. The rapid digitization of healthcare systems has resulted in the availability of large-scale longitudinal patient records, including electronic health records (EHRs), laboratory reports, medication histories, and diagnostic timelines. These temporally ordered datasets present unique opportunities for developing intelligent deep learning models capable of capturing complex temporal dependencies and nonlinear clinical patterns. This review systematically examines recent advances in deep learning frameworks designed for early clinical risk prediction using longitudinal patient data. We analyze recurrent neural networks, attention-based models, temporal convolutional networks, graph neural networks, and transformer architectures, highlighting their methodological innovations and comparative performance across diverse clinical applications such as sepsis detection, cardiovascular risk prediction, and chronic disease progression modeling. The review further explores feature representation strategies, handling of missing and irregular time-series data, evaluation protocols, and explainability mechanisms essential for clinical adoption. Key challenges—including data heterogeneity, model interpretability, privacy preservation, and deployment barriers—are critically discussed. Finally, we outline emerging research directions emphasizing trustworthy AI, federated learning, and multimodal integration to enhance predictive accuracy and real-world clinical applicability.
1.1 Rising Diagnostic Costs and the Need for Early Risk Stratification Healthcare systems globally are experiencing escalating diagnostic and treatment expenditures, particularly due to late-stage disease detection and avoidable hospital readmissions. Chronic illnesses such as cardiovascular disease, diabetes, and sepsis impose substantial economic and societal burdens when not identified early (World Health Organization, 2023). Early risk stratification enables proactive intervention, optimized resource allocation, and improved patient outcomes. Traditional risk scoring systems—such as logistic regression–based clinical scores— often rely on static or manually engineered features, limiting their ability to capture complex temporal interactions embedded within longitudinal patient data (Goldstein et al., 2017). Deep learning frameworks, especially temporal models such as LSTM and Transformer architectures, offer enhanced capability to model evolving clinical states and dynamic risk trajectories over time (Shickel et al., 2018). Consequently, there is growing interest in intelligent frameworks that integrate longitudinal analytics with predictive modeling for early-stage clinical decision support.
1.2 Conceptual Foundations
Key Words: Deep learning; Longitudinal patient records; Clinical risk prediction; Electronic health records (EHR); Temporal modeling; Explainable artificial intelligence; Healthcare analytics.
1.2.1 Clinical Risk Clinical risk refers to the probabilistic likelihood of a patient developing a specific adverse health outcome within a defined time horizon, such as disease onset, complication progression, hospitalization, or mortality. Risk prediction models aim to estimate this probability based on patient demographics, clinical measurements, medical history, laboratory values, and treatment patterns. Modern predictive frameworks increasingly move beyond singleoutcome binary classification toward time-to-event modeling and dynamic risk updating (Rajkomar et al., 2018). The shift toward continuous risk estimation aligns with the goals of precision medicine, where individualized predictions inform tailored therapeutic strategies.
1. INTRODUCTION The integration of artificial intelligence into healthcare analytics has significantly transformed predictive medicine, particularly in early-stage clinical risk identification. The increasing availability of longitudinal electronic health records (EHRs) provides an unprecedented opportunity to model disease trajectories and detect adverse outcomes before clinical deterioration becomes evident. Deep learning techniques, capable of extracting hierarchical and temporal representations from high-dimensional medical data, have demonstrated superior predictive performance compared to
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