Simulating Personalized Treatment Pathways in Chronic Disease Management Using Reinforcement Learning and Synthetic Patient Data

Abstract

We have created a method for developing personalized treatment plans using synthetic patient data and reinforcement learning; these methods can improve physician clinical decisions based on a patient's symptoms, adherence to their prescribed treatments and safety concerns. Our results show that as our model learns, it selects progressively better treatment options than before, avoids unsafe behavior, and helps patients across all characteristics (or patient profiles). Ablation studies also confirm that tracking symptoms and the prescribed medication schedule were key elements in achieving positive patient outcomes compared to other methodologies that are based on rules. Our trained model outperformed other models based on rules regarding reward, safety and symptom improvement. While the data used in the simulation environment, the results provide a level of realism that should aid clinicians wishing to develop new and safer treatment options.

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