Predicting ICU In-Hospital Mortality Using Adaptive Transformer Layer Fusion

Han Wang; Ruoyun He; Guoguang Lao; Ting Liu; Hejiao Luo; Changqi Qin; Hongying Luo; Junmin Huang; Zihan Wei; Lu Chen; Yongzhi Xu; Ziqian Bi; Junhao Song; Tianyang Wang; Chia Xin Liang; Xinyuan Song; Huafeng Liu; Junfeng Hao; Chunjie Tian

arXiv · 2506.04924

Predicting ICU In-Hospital Mortality Using Adaptive Transformer Layer Fusion

Han Wang·Ruoyun He·Guoguang Lao·Ting Liu·Hejiao Luo·Changqi Qin·Hongying Luo·Junmin Huang·Zihan Wei·Lu Chen·Yongzhi Xu·Ziqian Bi·Junhao Song·Tianyang Wang·Chia Xin Liang·Xinyuan Song·Huafeng Liu·Junfeng Hao·Chunjie Tian

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Abstract

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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Han Wang, Ruoyun He, Guoguang Lao, Ting Liu, Hejiao Luo, Changqi Qin, Hongying Luo, Junmin Huang, Zihan Wei, Lu Chen, Yongzhi Xu, Ziqian Bi, Junhao Song, Tianyang Wang, Chia Xin Liang, Xinyuan Song, Huafeng Liu, Junfeng Hao, Chunjie Tian. 2025-06-06. Predicting ICU In-Hospital Mortality Using Adaptive Transformer Layer Fusion. https://arxiv.org/abs/2506.04924

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