Quantum Feature Encoding–Driven Early Warning System for Sepsis and Organ Failure Using Hybrid Quantum–Classical Learning in ICUs
Tamilselvan Arjunan, Gopalakrishnan Arjunan, N. Jagadish Kumar +2 more
Sepsis and the development of multi-organ failure still constitute significant causes of death in the ICU, hence the necessity to develop predictive systems that could detect the fact of subtle physiological deviations before clinical deterioration. Despite the potential of classical machine learning models, including logistic regression, random forests and deep neural networks to predict sepsis, they frequently encounter challenges with large-dimensional ICU time-series data, nonlinear interaction of features and ability to predict sepsis at an early stage. These also models are sensitive to missing values and have limited cross-hospital generalizability and high rates of false-alarms which makes them difficult to implement in clinical settings, this paper proposes a Quantum-Assisted Early Warning System (QAEWS) based on quantum feature encoding and hybrid quantum-classical based learning models that provides better early sepsis and organ dysfunction detection. Amplitude and phase encoding the ICU physiological measures, lab measurements, and short-term vital sign traces are encoded into quantum-enhanced representations, which allow complexer modeling of complicated time-dependent relationships in a high-dimensional Hilbert space. The predictive architecture combines the Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) and supplemented by the classical neural layers to improve classification accuracy and false alerts. Model performance was compared with the Logistic Regression, Random Forest, XGBoost, and LSTM models using the same feature sets in the MIMIC-IV and eICU datasets. The quantum-assisted framework achieved a 7–12% increase in AUROC, a 14–18% reduction in false alarms, and provided predictions 1.5–2.8 hours earlier than classical baselines. Sensitivity at 85% specificity improved by 10–15%. These results demonstrate that quantum-enhanced feature spaces offer superior discrimination of early sepsis patterns, establishing QAEWS as a robust and clinically valuable approach for real-time ICU early warning.
International Conference on Computing, Communication, Control and Cyber-Physical Systems (I5CPS)•Feb 26, 2026•0 citations