A Big Data Pipeline Approach for Predicting Real-Time Pandemic Hospitalization Risk

Abstract

Pandemics emphasize the importance of real-time, interpretable clinical decision-support systems for identifying high-risk patients and assisting with prompt triage, particularly in data-intensive healthcare systems. This paper describes a novel dual big-data pipeline that includes (i) a streaming module for real-time epidemiological hospitalization risk prediction and (ii) a supplementary imaging-based detection and reasoning module for chest X-rays, with COVID-19 as an example. The first pipeline uses state-of-the-art machine learning algorithms to estimate patient-level hospitalization risk based on data from the Centers for Disease Control and Prevention’s (CDC) COVID-19 Case Surveillance dataset. A Bloom filter accelerated triage by constant-time pre-screening of high-risk profiles. Specifically, after significant experimentation and optimization, one of the models, XGBoost, was selected because it achieved the best minority-class F1-score (0.76) and recall (0.80), outperforming baseline models. Synthetic data generation was employed to mimic streaming workloads, including a strategy that used the Conditional Tabular Generative Adversarial Network (CTGAN) to produce the best balanced and realistic distributions. The second pipeline focuses on diagnostic imaging and combines an advanced convolutional neural network, EfficientNet-B0, with Grad-CAM visual explanations, achieving 99.5% internal and 99.3% external accuracy. A lightweight Generative Pre-trained Transformer (GPT)-based reasoning layer converts model predictions into auditable triage comments (ALERT/FLAG/LOG), yielding traceable and interpretable decision logs. This scalable, explainable, and near-real-time framework provides a foundation for future multimodal and genomic advancements in public health readiness.

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