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

TL;DR

A novel Transformerbased multi-label learning framework (IAENet) that combines an improved Time-Aware Feature-wise Linear Modulation module for static covariates and dynamic variables robust fusion and complex temporal dependencies modeling and introduces a Label-Constrained Reweighting Loss with co-occurrence regularization to effectively mitigate intra-event imbalance.

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 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.

Figures (5)

• Figure 1: Positive sample distribution by adverse event for 5, 10, and 15 minutes prediction windows in MuAE. Percentages indicate the event occurrence rate among total samples.
• Figure 2: The pipeline of Multi-adverse Events Early Warning
• Figure 3: An overview of the IAENet framework for time-series in multi-label classification. Given an sample about vital sign series and static covariate in MuAE dataset $\mathbf{x} = \{\mathbf{x}_{d_0},..., \mathbf{x}_{d_{14}}, \mathbf{x}_{s_0},..., \mathbf{x}_{s_4} \}$, our goal is to predict whether the values in the following $\triangle$ time steps will be normal or abnormal $\mathbf{y} = \{\mathbf{y}_{1}, \mathbf{y}_{2}, ..., \mathbf{y}_{6} \}$. Firstly, the preprocessed dynamic variables and static covariates are respectively fused through the TAFiLM module for feature early fusion. Then, a Transformer encoder is employed to capture temporal correlations among multivariate variables. Finally, the model is trained using the proposed LCRLoss, which combines a batch-wise label frequency-weighted BCE loss with a co-occurrence constraint term based on label dependencies.
• Figure 4: Derivatives of the loss functions. The X-axis denotes the logit of positive labels, and the Y-axis is the corresponding gradients.
• Figure 5: Co-occurrence matrix of adverse events