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Bibliographic Details
Main Authors:	Wang, Han, He, Ruoyun, Lao, Guoguang, Liu, Ting, Luo, Hejiao, Qin, Changqi, Luo, Hongying, Huang, Junmin, Wei, Zihan, Chen, Lu, Xu, Yongzhi, Bi, Ziqian, Song, Junhao, Wang, Tianyang, Liang, Chia Xin, Song, Xinyuan, Liu, Huafeng, Hao, Junfeng, Tian, Chunjie
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2506.04924
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Table of Contents:

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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