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

TL;DR

This paper introduces a Transformer-based multi-label learning framework, IAENet, for early warning of intraoperative adverse events, addressing data heterogeneity, event dependencies, and class imbalance to improve prediction accuracy.

Contribution

It constructs the first multi-label adverse events dataset and proposes a novel IAENet model with specialized modules and loss functions for better intraoperative event prediction.

Findings

IAENet outperforms baselines on early warning tasks by up to 7.57% in F1 score.

The proposed model effectively models complex temporal dependencies and event co-occurrences.

Experimental results demonstrate significant improvements in intraoperative adverse event prediction.

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.