IOH_Transformer v1.0 This repository contains the implementation of a Transformer-based deep learning model for real-time prediction of intraoperative hypotension (IOH) using dynamic time-series vital sign data. Study Overview Transient intraoperative hypotension (IOH) is associated with adverse postoperative outcomes, yet many existing prediction models rely on high-resolution waveform data that are not routinely available in clinical practice. This project presents a Transformer-based deep learning framework designed to predict IOH in real time using routinely collected vital sign time-series data. Dataset The model was developed using a retrospective dataset of 319,699 surgical cases from a tertiary hospital in China (2013–2023) and externally validated using an independent dataset from South Korea. Model Performance The Transformer model demonstrated strong predictive performance: 5-minute prediction horizon: AUC = 0.904 10-minute prediction horizon: AUC = 0.892 15-minute prediction horizon: AUC = 0.882 Recall ≥ 88.3% Compared with XGBoost: Transformer showed higher recall and better probability calibration XGBoost achieved higher accuracy and specificity External validation confirmed the generalizability of both models. Clinical Relevance A nested cohort analysis showed that IOH burden (cumulative MAP ≤65/60/55 mm Hg in mm Hg·min) was significantly associated with postoperative acute kidney injury (AKI) and acute kidney disease (AKD). Key Features Transformer-based deep learning architecture Real-time IOH risk prediction Time-series vital sign modeling External validation Clinical outcome association analysis Citation If you use this code in your research, please cite: Zhu S., et al. Transformer-Based Deep Learning Model for Real-Time Prediction of Intraoperative Hypotension Using Dynamic Time-Series Vital Signs: a retrospective study License Please refer to the repository license for usage terms.