VitalBench: A Rigorous Multi-Center Benchmark for Long-Term Vital Sign Prediction in Intraoperative Care
Xiuding Cai, Xueyao Wang, Sen Wang, Yaoyao Zhu, Jiao Chen, Yu Yao

TL;DR
VitalBench is a comprehensive benchmark dataset and evaluation framework for intraoperative vital sign prediction, promoting standardized, robust, and cross-center validated deep learning models in clinical settings.
Contribution
We introduce VitalBench, the first standardized multi-center benchmark for intraoperative vital sign prediction, facilitating fair comparison and development of robust predictive models.
Findings
VitalBench includes data from over 4,000 surgeries across two centers.
The benchmark supports evaluation on complete, incomplete, and cross-center data.
VitalBench promotes model robustness and generalization in clinical environments.
Abstract
Intraoperative monitoring and prediction of vital signs are critical for ensuring patient safety and improving surgical outcomes. Despite recent advances in deep learning models for medical time-series forecasting, several challenges persist, including the lack of standardized benchmarks, incomplete data, and limited cross-center validation. To address these challenges, we introduce VitalBench, a novel benchmark specifically designed for intraoperative vital sign prediction. VitalBench includes data from over 4,000 surgeries across two independent medical centers, offering three evaluation tracks: complete data, incomplete data, and cross-center generalization. This framework reflects the real-world complexities of clinical practice, minimizing reliance on extensive preprocessing and incorporating masked loss techniques for robust and unbiased model evaluation. By providing a…
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Taxonomy
TopicsSepsis Diagnosis and Treatment · Machine Learning in Healthcare · Artificial Intelligence in Healthcare and Education
