Deep Attention Q-Network for Personalized Treatment Recommendation
Simin Ma Junghwan Lee Nicoleta Serban Shihao Yang
Tailoring treatment for individual patients is crucial yet challenging in
order to achieve optimal healthcare outcomes. Recent advances in reinforcement
learning offer promising personalized treatment recommendations; however, they
rely solely on current patient observations (vital signs, demographics) as the
patient's state, which may not accurately represent the true health status of
the patient. This limitation hampers policy learning and evaluation, ultimately
limiting treatment effectiveness. In this study, we propose the Deep Attention
Q-Network for personalized treatment recommendations, utilizing the Transformer
architecture within a deep reinforcement learning framework to efficiently
incorporate all past patient observations. We evaluated the model on real-world
sepsis and acute hypotension cohorts, demonstrating its superiority to
state-of-the-art models. The source code for our model is available at
https://github.com/stevenmsm/RL-ICU-DAQN.