Deep Deterministic Policy Gradient and Attention-Driven Clinical Decision Support Framework

Abstract

A Clinical Decision Support System (CDSS) is a discrete and information based technology which is implemented into a modern health care system to assist clinicians and health specialties to make sophisticated decisions that enhance patient welfare. The older models of CDSS have a lot of problems trying to adapt with and cope with the new situations. Always using a static approach to dealing with complex patient data pieces with no dynamic reasoning could result in simple decision making, failing to capture the essence of personalized medical treatment. This study attempts to blend the Deep Deterministic Policy Gradient (DDPG) a reinforcement algorithm with attention mechanisms to tackle those issues and enhance the higher level decision-making capabilities and show the comparison with the baseline DDPG to demonstrate the improvement over base model. DDPG, which is based on actor-critic model, allows learning within a continuous space of action to address treatment plan tailoring which is responsive to evolving patient conditions, while the attention mechanism filters patient data to ensure that the most compelling features and symptoms are incorporated. Attention mechanism is also able to redefine the context making it more personalized. This groundbreaking synergy empowers the system to face a range of medical situations such as chronic illnesses, cancer, infectious diseases, and more. More importantly though, it surpasses the range of sophisticated CDSS models while gaining flexibility along the way.

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