Optimizing Clinical Decision Support Systems Using Quantum-Inspired Reinforcement Learning for Real-Time Patient Risk Stratification

Arthur Luis Alexis

Authors

Arthur Luis Alexis Clinical Informaticist, Spain. Author

Keywords:

Quantum-inspired computing, Reinforcement learning, Clinical decision support, Risk stratification, Real-time prediction, Sepsis detection

Abstract

Clinical Decision Support Systems (CDSS) play a critical role in improving diagnostic accuracy and enhancing clinical decision-making. Yet, real-time patient risk stratification remains a significant computational challenge, particularly in high-acuity settings where data variability and complexity are high.

This study introduces a novel approach that integrates Quantum-Inspired Reinforcement Learning (QiRL) into CDSS frameworks to address these limitations. By leveraging quantum probabilistic representations alongside reinforcement learning algorithms, the proposed model enhances adaptive learning in dynamic clinical environments.

The QiRL-enhanced CDSS was evaluated using synthetic intensive care unit (ICU) data, focusing on early sepsis detection as a use case. Experimental results indicate that the QiRL-based system achieved superior predictive accuracy and faster convergence compared to conventional machine learning models, demonstrating its potential for real-time risk assessment in critical care. This research highlights the promise of quantum-inspired methodologies in advancing intelligent clinical systems, offering a scalable and efficient solution for timely intervention and personalized patient management.

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