用模仿学习建模儿童体外膜肺支持治疗决策，提升临床辅助能力。
Imitation learning for clinical decision support in pediatric ECMO
- 基于观测数据，用模仿学习推断未直接记录的医疗操作
- TabPFN模型在真实儿科ECMO数据上优于XGBoost和MLP等基线
- 适合临床决策支持系统研究者参考，尤其关注小样本高复杂场景
儿童重症监护是一个动态且高风险的过程，涉及持续监测与生命支持治疗的不断调整。建模这些干预措施对有效决策支持至关重要。针对儿童体外膜肺氧合（ECMO）中高复杂性与数据稀缺的挑战，本文将临床决策建模为从轨迹中学习行动策略，即模仿学习——从观察数据中学习动作模型，其中动作本身未被直接观测。我们采用近期基于Transformer的表格数据方法TabPFN，以及传统基线如XGBoost和多层感知机（MLPs），在真实世界儿科ECMO数据上学习动作模型。结果表明，基于TabPFN的方法始终优于经典基线，支持其作为儿科ECMO决策支持中医生行为建模的有力基准。
原文摘要 · Abstract (English)
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.
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