Quantum-Inspired Optimization with Explainable Deep Learning for Precision Medicine and Personalized Treatment Recommendation Systems

Abstract

However, traditional methods may be lacking in terms of interpretability, treatment personalisation, and efficacy, safety, adherence, and cost optimisation. This paper introduces a novel framework that combines an optimization approach inspired by quantum computing with the explainable deep learning to predict treatment response and to rank personalized treatment alternatives. A single feature-fusion model represents multimodal synthetic patient profiles with variables related to demographics, clinical, laboratory, genomic, lifestyle and treatment history. An explainable AI determines the factors affecting each treatment recommendation and a quantum-inspired evolutionary algorithm chooses the best possible treatment for each patient while excluding the ones that are contraindicated. The framework is projected to achieve a 92.4% accuracy of treatment response, an ROC-AUC of 0.96, top-three recommendation accuracy of 96.7%, improvement in treatment utility of 11.8% and a reduction in the risk of adverse events of 14.2% under the defined scenario-based analytical assumptions. These are only projected results and clinical, software and hardware tests would be required to prove the framework's potential.