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JCSIS

Optimized Bidirectional LSTM Networks for Real-Time Sepsis Prediction in Emergency Department Settings.

Weiguo Gee
Volume 5

Abstract

Sepsis is a life-threatening clinical condition that requires rapid recognition and timely intervention, particularly in Emergency Department (ED) settings where patients often present with heterogeneous symptoms, incomplete medical histories, and rapidly changing physiological status. Delayed diagnosis of sepsis may lead to septic shock, multi-organ failure, prolonged hospitalization, and increased mortality. Although conventional screening tools such as SIRS, qSOFA, MEWS, and NEWS provide practical bedside assessment, their predictive performance is often limited by static threshold-based rules and insufficient ability to capture complex temporal patterns in patient deterioration. To address these limitations, this paper proposes an optimized Bidirectional Long Short-Term Memory (BiLSTM) network for real-time sepsis prediction using time-series Electronic Health Record data collected during ED admission.

The proposed framework is designed to process dynamic clinical variables, including vital signs, laboratory test results, demographic characteristics, triage information, comorbidities, medication records, and early clinical observations. A comprehensive preprocessing pipeline is applied to handle missing values, align irregularly sampled measurements, normalize continuous variables, encode categorical features, and construct time-windowed patient trajectories suitable for sequential modeling. The BiLSTM architecture is employed to learn bidirectional temporal dependencies from evolving patient data, enabling the model to capture both preceding clinical trends and contextual relationships among physiological measurements. To enhance predictive accuracy and convergence efficiency, the network is optimized using advanced hyperparameter tuning strategies, including Bayesian optimization and metaheuristic algorithms such as Particle Swarm Optimization and Grey Wolf Optimizer. These optimization methods are used to determine the optimal learning rate, number of hidden units, dropout rate, batch size, time-window length, and activation configuration.

The proposed model can be trained and validated using large-scale publicly available clinical datasets, such as MIMIC-IV and MIMIC-IV-ED, as well as institutional ED datasets where available. Sepsis labels are generated based on clinically accepted criteria involving suspected infection and organ dysfunction, while prediction horizons are defined to support early warning at clinically meaningful intervals before sepsis onset. To address class imbalance caused by the relatively lower proportion of sepsis cases compared with non-sepsis encounters, the framework incorporates class-weighted loss functions, focal loss, and resampling strategies. Model performance is evaluated using AUC-ROC, AUC-PR, sensitivity, specificity, F1-score, calibration measures, and lead-time analysis, with comparisons against traditional clinical scoring systems, standard LSTM, GRU, Random Forest, XGBoost, and conventional machine learning baselines.

Experimental results are expected to demonstrate that the optimized BiLSTM model achieves superior early prediction performance by effectively modeling temporal deterioration patterns and reducing false negative alerts. In addition, explainability techniques such as SHAP, attention-based visualization, and patient-level temporal risk attribution are incorporated to identify the most influential clinical indicators contributing to sepsis risk, including abnormal respiratory rate, hypotension, elevated lactate level, leukocyte count changes, fever, oxygen saturation decline, renal function markers, and altered mental status. These explanations support clinician trust by clarifying how specific physiological trends influence the predicted risk score over time.

The proposed framework offers a scalable, accurate, and interpretable real-time decision-support system for early sepsis prediction in emergency care environments. By integrating optimized sequential deep learning with clinically meaningful temporal risk analysis, the system can assist ED clinicians in prioritizing high-risk patients, initiating timely diagnostic evaluation, and improving early intervention strategies. Its real-time design makes it suitable for integration into hospital EHR systems, triage dashboards, and automated early warning platforms, with strong potential to improve patient outcomes and reduce the clinical burden associated with delayed sepsis recognition.

Keywords

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