The COVID-19 pandemic has posed a heavy burden to the healthcare system worldwide and caused huge social disruption and economic loss. Many deep learning models have been proposed to conduct clinical predictive tasks such as mortality prediction for COVID-19 patients in intensive care units using Electronic Health Record (EHR) data. Despite their initial success in certain clinical applications, there is currently a lack of benchmarking results to achieve a fair comparison so that we can select the optimal model for clinical use. Furthermore, there is a discrepancy between the formulation of traditional prediction tasks and real-world clinical practice in intensive care. To fill these gaps, we propose two clinical prediction tasks, Outcome-specific length-of-stay prediction and Early mortality prediction for COVID-19 patients in intensive care units. The two tasks are adapted from the naive length-of-stay and mortality prediction tasks to accommodate the clinical practice for COVID-19 patients. We propose fair, detailed, open-source data-preprocessing pipelines and evaluate 17 state-of-the-art predictive models on two tasks, including 5 machine learning models, 6 basic deep learning models and 6 deep learning predictive models specifically designed for EHR data. We provide benchmarking results using data from two real-world COVID-19 EHR datasets. Both datasets are publicly available without needing any inquiry and one dataset can be accessed on request. We provide fair, reproducible benchmarking results for two tasks. We deploy all experiment results and models on an online platform. We also allow clinicians and researchers to upload their data to the platform and get quick prediction results using our trained models. We hope our efforts can further facilitate deep learning and machine learning research for COVID-19 predictive modeling.
翻译:COVID-19大流行给全世界的保健系统带来了沉重的负担,并造成了巨大的社会混乱和经济损失。许多深层次的学习模式被提出来进行临床预测任务,例如利用电子健康记录(EHR)数据对特护单位的COVID-19病人进行死亡率预测。尽管它们在某些临床应用中最初取得了成功,但目前缺乏基准结果,无法进行公平的比较,以便我们能够选择最佳临床使用模式。此外,传统预测任务的制定与实际世界临床密集护理做法之间存在差异。为填补这些差距,我们建议执行两个临床预测任务:结果特定的长期预测和密集护理单位的COVID-19病人早期死亡率预测。两项任务根据天真的停留时间和死亡率预测任务加以调整,以适应COVI-19病人的临床做法。我们提出公平、详细、开放的源码数据处理管道,并对两项任务的17个最新预测模型进行评估,包括5个深层次的计算机学习模型、6个基础深层次的学习模型和6个深层次的人力资源预测模型,专门设计用于EHR-D数据。我们利用两个实验实验室的数据来进行基准化研究。我们用两个数据库进行数据检索的数据,可以提供两种数据快速数据检索。我们通过两个数据库进行数据检索的数据,可以提供一种数据,我们进行一项数据检索。我们通过两个数据库进行基准数据检索。我们用两个数据进行基准数据检索数据进行数据,我们提供一个数据,可以提供一种数据,可以提供一种数据,可以提供一种数据进行基准数据,可以提供一种数据,可以提供一种数据进行基准数据。我们所进行基准数据。