Imitation learning for clinical decision support in pediatric ECMO
On the map: Offline RL and imitation learning from data → Safe offline RL
Abstract (arXiv)
Pediatric critical care is a dynamic, high-stakes process involving constant monitoring and adjustments in life-saving treatments. Modeling these interventions is crucial for effective decision support. To address the challenges of high complexity and data scarcity in pediatric Extracorporeal Membrane Oxygenation (ECMO), we frame clinical decision-making as learning to act from trajectories, i.e., imitation learning that learns action models from observational data, with a key feature that actions are not directly observed. We consider TabPFN, a recent transformer-based approach for tabular data, and traditional baselines including XGBoost and Multi-Layer Perceptrons(MLPs) on real-world pediatric ECMO data to learn the action models. We find that the TabPFN-based approach consistently outperforms these classical baselines, supporting its use as a strong clinician-behavior baseline for pediatric ECMO decision support.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Safe offline RL, one of 13 directions in Offline RL and imitation learning from data. Builds on 5 of its 11 references that are in the map. First posted on arXiv 15 May 2026.
Builds on
5 of 11 references.
References · 5
- 2017Attention Is All You Needcited by 59,932
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,120
- 2022TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Secondcited by 643
- 2025TabPFN-2.5: Advancing the State of the Art in Tabular Foundation Modelscited by 115
- 2024Generative Artificial Intelligence: A Systematic Review and Applicationscited by 37
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- 2023Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
- 2026VentAgent: When LLMs Learn to Breathe -- Multi-Objective Arbitration for ARDS Ventilation
- 2025Guardian-regularized Safe Offline Reinforcement Learning for Smart Weaning of Mechanical Circulatory Devices
- 2026A Multimodal Deep Learning Framework for Predicting ICU Deterioration: Integrating ECG Waveforms with Clinical Data and Clinician Benchmarking
- 2026Offline Reinforcement Learning for Hemodynamic Management of Sepsis in the ICU: a MIMIC-IV Study with Dual Off-Policy Evaluation
- 2025Exploring a Graph-based Approach to Offline Reinforcement Learning for Sepsis Treatment
- 2024Pruning the Path to Optimal Care: Identifying Systematically Suboptimal Medical Decision-Making with Inverse Reinforcement Learning
- 2025Advancing Safe Mechanical Ventilation Using Offline RL With Hybrid Actions and Clinically Aligned Rewards