Key result
A data-driven framework integrating offline and constrained reinforcement learning reduced estimated mortality risk compared to observed clinical practices while satisfying LOS and readmission thresholds.
Why the study?
ICU discharge involves a difficult tradeoff between readmission or mortality risks and capacity strain, creating a need for personalized discharge policies under clinical and operational constraints.
Does a reinforcement learning framework reduce estimated mortality risk and satisfy clinical constraints for ICU discharge compared to observed clinical practices?
Does a reinforcement learning framework reduce estimated mortality risk and satisfy clinical constraints for ICU discharge compared to observed clinical practices?
Combining offline and constrained reinforcement learning can derive personalized ICU discharge policies that outperform observed clinical practices in estimated mortality risk.
May inform constrained RL for ICU discharge; hypothesis-generating and requires prospective validation before practice change.
Effective discharge decision-making in intensive care units (ICUs) is crucial for optimizing patient outcomes and ensuring efficient utilization of limited resources, a challenge exacerbated during the COVID-19 pandemic. Discharge decision-making involves a tradeoff: early discharge increases the risk of ICU readmission and mortality, whereas prolonged ICU stays raise costs and strain capacity. We develop a data-driven framework that integrates offline reinforcement learning (offline RL) with constrained reinforcement learning (constrained RL) to derive personalized ICU discharge policies. We consider the decision-making process with multiple ICU readmissions, and readmission count is incorporated into the state space. The framework also explicitly incorporates clinical and operational constraints, including ICU length of stay (LOS) and readmission risk. Using real-world medical data from the MIMIC-IV database, we train and evaluate the framework with sensitivity and robustness analysis . The learned policiesreduce esimated mortality risk while satisfying predefined thresholds on LOS and readmission risk. Numerical results also show that the learned policies outperform observed clinical practices in mortality estimates and demonstrate conservative decision behaviors for high-risk patients. Overall, this work highlights the feasibility and value of combining offline and constrained RL for critical care decisoin support.
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Sun et al. (2025) studied Intensive care unit (ICU) patients. Data-driven framework integrating offline and constrained reinforcement learning vs. Observed clinical practices was evaluated on Estimated mortality risk, ICU length of stay (LOS), and readmission risk. A data-driven framework integrating offline and constrained reinforcement learning reduced estimated mortality risk compared to observed clinical practices while satisfying LOS and readmission thresholds.
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