PDF(Pubmed)
Abstract：
The COVID-19 pandemic highlighted the need for predictive deep-learning models in health care. However, practical prediction task design, fair comparison, and model selection for clinical applications remain a challenge. To address this, we introduce and evaluate 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 and traditional machine-learning, basic deep-learning, and advanced deep-learning models, tailored for electronic health record (EHR) data. Benchmarking results from two real-world COVID-19 EHR datasets are provided, and all results and trained models have been 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.
摘要：
COVID-19大流行强调了在医疗保健中需要预测性深度学习模型。然而,实际预测任务设计，公平的比较，和临床应用的模型选择仍然是一个挑战。为了解决这个问题，我们引入并评估了两项新的预测任务-针对重症监护患者的结局特异性住院时间和早期死亡率预测-这两个任务更好地反映了临床现实.我们开发了评估指标，模型自适应设计，以及这些任务的开源数据预处理管道，同时还评估18个预测模型，包括临床评分方法和传统的机器学习，基本的深度学习，和先进的深度学习模型，为电子健康记录(EHR)数据量身定制。提供了来自两个真实世界COVID-19EHR数据集的基准结果，所有结果和训练模型都已在在线平台上发布，供临床医生和研究人员使用。我们的努力有助于推进流行病预测建模中的深度学习和机器学习研究。
