Early Warning of Intraoperative Adverse Events via Transformer-Driven Multi-Label Learning
This work provides an incremental improvement in early warning systems for intraoperative adverse events, aiming to enhance patient safety during surgery.
This paper addresses the challenge of early warning for intraoperative adverse events by developing a multi-label learning framework. The proposed IAENet model, which incorporates a novel Transformer-based architecture and a specialized loss function, achieved average F1 score improvements of +5.05%, +2.82%, and +7.57% for 5, 10, and 15-minute early warning tasks, respectively, compared to strong baselines.
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 frequently co-occurring events. Extensive experiments demonstrate that IAENet consistently outperforms strong baselines on 5, 10, and 15-minute early warning tasks, achieving improvements of +5.05%, +2.82%, and +7.57% on average F1 score. These results highlight the potential of IAENet for supporting intelligent intraoperative decision-making in clinical practice.