用自适应融合注意力模型，从病历文本中提前预测重症患者死亡风险。
Predicting ICU In-Hospital Mortality Using Adaptive Transformer Layer Fusion
- 通过自适应加权融合BERT多层特征，结合低秩适配器训练。
- 在cw-24数据集上AUPRC优于主流分类器，兼顾精准与召回平衡。
- 可无缝对接树模型或深度网络，适合临床早期预警系统部署。
早期识别高危重症患者对合理分配医疗资源至关重要。本文提出ALFIA（自适应层融合智能注意力），一种基于注意力的模块化架构，联合训练LoRA适配器与自适应层权重机制，融合BERT主干网络的多层语义特征。在严格的cw-24基准上训练，ALFIA在AUPRC指标上超越现有表格分类器，同时保持良好的精确率-召回率平衡。ALFIA融合模块生成的嵌入向量，捕捉细粒度临床线索与高层概念，可无缝衔接GBDT（CatBoost/LightGBM）构成ALFIA-boost，或与深度神经网络结合为ALFIA-nn，进一步提升性能。实验验证了ALFIA在早期预警中的优越表现——直接处理常规临床文本，为重症监护场景下的风险分层与及时干预提供便捷而稳健的工具。
原文摘要 · Abstract (English)
Early identification of high-risk ICU patients is crucial for directing limited medical resources. We introduce ALFIA (Adaptive Layer Fusion with Intelligent Attention), a modular, attention-based architecture that jointly trains LoRA (Low-Rank Adaptation) adapters and an adaptive layer-weighting mechanism to fuse multi-layer semantic features from a BERT backbone. Trained on our rigorous cw-24 (CriticalWindow-24) benchmark, ALFIA surpasses state-of-the-art tabular classifiers in AUPRC while preserving a balanced precision-recall profile. The embeddings produced by ALFIA's fusion module, capturing both fine-grained clinical cues and high-level concepts, enable seamless pairing with GBDTs (CatBoost/LightGBM) as ALFIA-boost, and deep neuro networks as ALFIA-nn, yielding additional performance gains. Our experiments confirm ALFIA's superior early-warning performance, by operating directly on routine clinical text, it furnishes clinicians with a convenient yet robust tool for risk stratification and timely intervention in critical-care settings.
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