[2603.05212] Early Warning of Intraoperative Adverse Events via Transformer-Driven Multi-Label Learning

Computer Science > Machine Learning arXiv:2603.05212 (cs) [Submitted on 5 Mar 2026] Title:Early Warning of Intraoperative Adverse Events via Transformer-Driven Multi-Label Learning Authors:Xueyao Wang, Xiuding Cai, Honglin Shang, Yaoyao Zhu, Yu Yao View a PDF of the paper titled Early Warning of Intraoperative Adverse Events via Transformer-Driven Multi-Label Learning, by Xueyao Wang and Xiuding Cai and Honglin Shang and Yaoyao Zhu and Yu Yao View PDF HTML (experimental) Abstract:Early warning of intraoperative adverse events plays a vital role in reducing surgical risk and improving patient safety. While deep learning has shown promise in predicting the single adverse event, several key challenges remain: overlooking adverse event dependencies, underutilizing heterogeneous clinical data, and suffering from the class imbalance inherent in medical datasets. To address these issues, we construct the first Multi-label Adverse Events dataset (MuAE) for intraoperative adverse events prediction, covering six critical events. Next, we propose a novel Transformerbased multi-label learning framework (IAENet) that combines an improved Time-Aware Feature-wise Linear Modulation (TAFiLM) module for static covariates and dynamic variables robust fusion and complex temporal dependencies modeling. Furthermore, we introduce a Label-Constrained Reweighting Loss (LCRLoss) with co-occurrence regularization to effectively mitigate intra-event imbalance and enforce structured consistency among ...