用对比学习提升儿科呼吸机管理跨机构知识迁移效果
Contrastive Representation Learning Helps Cross-institutional Knowledge Transfer: A Study in Pediatric Ventilation Management
- 采用对比预测编码学习临床时序数据表征
- 小样本下微调后模型跨机构性能显著提升
- 时间演变模式比诊疗决策更易迁移，适合小单位借鉴大中心经验
临床机器学习在不同机构间部署面临患者群体和临床实践差异大的挑战。本文提出系统性框架，通过对比预测编码（CPC）在一般儿科重症监护室（PICU）与心脏专科单位之间实现儿科呼吸机管理的跨机构知识迁移。研究考察不同数据配置与微调策略对知识转移的影响。结果表明，直接模型迁移表现差，而结合合适微调的CPC能有效实现机构间知识共享，尤其在数据有限场景中优势明显。迁移模式分析揭示重要不对称性：时间演变模式比床旁决策更易迁移，为跨机构临床决策支持系统部署提供可行路径。本研究为构建更具泛化能力的临床辅助系统提供了实证依据，使小型专科单位可借助大型中心的知识。
原文摘要 · Abstract (English)
Clinical machine learning deployment across institutions faces significant challenges when patient populations and clinical practices differ substantially. We present a systematic framework for cross-institutional knowledge transfer in clinical time series, demonstrated through pediatric ventilation management between a general pediatric intensive care unit (PICU) and a cardiac-focused unit. Using contrastive predictive coding (CPC) for representation learning, we investigate how different data regimes and fine-tuning strategies affect knowledge transfer across institutional boundaries. Our results show that while direct model transfer performs poorly, CPC with appropriate fine-tuning enables effective knowledge sharing between institutions, with benefits particularly evident in limited data scenarios. Analysis of transfer patterns reveals an important asymmetry: temporal progression patterns transfer more readily than point-of-care decisions, suggesting practical pathways for cross-institutional deployment. Through a systematic evaluation of fine-tuning approaches and transfer patterns, our work provides insights for developing more generalizable clinical decision support systems while enabling smaller specialized units to leverage knowledge from larger centers.
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