Personalized Treatment Recommendation

Personalized treatment recommendation aims to tailor medical interventions to individual patient characteristics for optimal outcomes, addressing the limitations of one-size-fits-all approaches. Current research heavily utilizes machine learning, employing diverse architectures like reinforcement learning (including multi-agent and Q-network variations), neural networks (for treatment effect estimation and subgroup identification), and extreme multilabel classification for efficient recommendation. This field is significant for improving healthcare efficacy and safety by optimizing treatment selection and dosage, impacting both clinical practice and the development of more sophisticated decision support systems.