Predicting the need for blood transfusion in intensive care units with reinforcement learning

Abstract

As critically ill patients frequently develop anemia or coagulopathy, transfusion of blood products is a frequent intervention in the Intensive Care Units (ICU). However, inappropriate transfusion decisions made by physicians are often associated with increased risk of complications and higher hospital costs. In this work, we aim to develop a decision support tool that uses available patient information for transfusion decision-making on three common blood products (red blood cells, platelets, and fresh frozen plasma). To this end, we adopt an off-policy batch reinforcement learning (RL) algorithm, namely, discretized Batch Constrained Q-learning, to determine the best action (transfusion or not) given observed patient trajectories. Simultaneously, we consider different state representation approaches and reward design mechanisms to evaluate their impacts on policy learning. Experiments are conducted on two realworld critical care datasets: the MIMIC-III and the UCSF. Results demonstrate that policy recommendations on transfusion achieved comparable matching against true hospital policies via accuracy and weighted importance sampling evaluations on the MIMIC-III dataset. Furthermore, a combination of transfer learning (TL) and RL on the data-scarce UCSF dataset can provide up to 17.02% improvement in terms of accuracy, and up to 18.94% and 21.63% improvement in jump-start and asymptotic performance in terms of weighted importance sampling averaged over three transfusion tasks. Finally, simulations on transfusion decisions suggest that the transferred RL policy could reduce patients' estimated 28-day mortality rate by 2.74% and decreased acuity rate by 1.18% on the UCSF dataset. In short, RL with appropriate patient state encoding and reward designs shows promise in treatment recommendations for blood transfusion and further optimizes the real-time treatment strategies by improving patients' clinical outcomes.

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“…• Robotic Surgery and Rehabilitation: RL is used in training robotic systems for surgery and rehabilitation, allowing them to adapt to patient-specific conditions and improve over time based on feedback from surgical outcomes or patient recovery rates [370]. • Personalized Medicine: RL models can analyze patient data over time to predict the most effective treatment plans, considering the unique health trajectory and response patterns of each patient [371], [372]. • Healthcare Resource Management: RL algorithms can assist in managing healthcare resources, such as hospital bed allocation, staff scheduling, and equipment usage, by learning optimal allocation strategies based on demand patterns and resource availability [373].…”