A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care

The COVID-19 pandemic highlighted the need for predictive deep learning models in healthcare. However, practical prediction task design, fair comparison and model selection for clinical applications remain a challenge. To address this, we introduced and evaluated two new prediction tasks - Outcome-specific length-of-stay and Early mortality prediction for COVID-19 patients in intensive care - which better reflect clinical realities. We developed evaluation metrics, model adaptation designs, and open-source data preprocessing pipelines for these tasks, while also evaluating 18 predictive models, including clinical scoring methods, traditional machine learning, basic deep learning, and advanced deep learning models tailored for EHR data. Benchmarking results from two real-world COVID-19 EHR datasets are provided, and all results and trained models are released on an online platform for use by clinicians and researchers. Our efforts contribute to the advancement of deep learning and machine learning research in pandemic predictive modeling.