AI RESEARCH
Early Warning of Intraoperative Adverse Events via Transformer-Driven Multi-Label Learning
arXiv CS.LG
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ArXi:2603.05212v2 Announce Type: replace 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.