School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Department of Internal Medicine, McGovern Medical School of The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
J Biomed Inform. 2022 Jun;130:104079. doi: 10.1016/j.jbi.2022.104079. Epub 2022 Apr 27.
The Coronavirus Disease 2019 (COVID-19) pandemic has overwhelmed the capacity of healthcare resources and posed a challenge for worldwide hospitals. The ability to distinguish potentially deteriorating patients from the rest helps facilitate reasonable allocation of medical resources, such as ventilators, hospital beds, and human resources. The real-time accurate prediction of a patient's risk scores could also help physicians to provide earlier respiratory support for the patient and reduce the risk of mortality.
We propose a robust real-time prediction model for the in-hospital COVID-19 patients' probability of requiring mechanical ventilation (MV). The end-to-end neural network model incorporates the Multi-task Gaussian Process to handle the irregular sampling rate in observational data together with a self-attention neural network for the prediction task.
We evaluate our model on a large database with 9,532 nationwide in-hospital patients with COVID-19. The model demonstrates significant robustness and consistency improvements compared to conventional machine learning models. The proposed prediction model also shows performance improvements in terms of area under the receiver operating characteristic curve (AUROC) and area under the precision-recall curve (AUPRC) compared to various deep learning models, especially at early times after a patient's hospital admission.
The availability of large and real-time clinical data calls for new methods to make the best use of them for real-time patient risk prediction. It is not ideal for simplifying the data for traditional methods or for making unrealistic assumptions that deviate from observation's true dynamics. We demonstrate a pilot effort to harmonize cross-sectional and longitudinal information for mechanical ventilation needing prediction.
2019 年冠状病毒病（COVID-19）大流行使医疗资源不堪重负，给世界各地的医院带来了挑战。能够将有潜在恶化风险的患者与其他患者区分开来有助于合理分配医疗资源，如呼吸机、病床和人力资源。对患者风险评分进行实时准确预测也有助于医生更早地为患者提供呼吸支持，降低死亡率。
我们提出了一种用于预测 COVID-19 住院患者需要机械通气（MV）概率的稳健实时预测模型。端到端神经网络模型结合了多任务高斯过程来处理观测数据中的不规则采样率，以及自注意力神经网络用于预测任务。
我们在一个包含全国 9532 名 COVID-19 住院患者的大型数据库上评估了我们的模型。与传统机器学习模型相比，该模型具有显著的稳健性和一致性改进。与各种深度学习模型相比，所提出的预测模型在接受者操作特征曲线下面积（AUROC）和精度-召回曲线下面积（AUPRC）方面也显示出性能提升，尤其是在患者住院后早期。
大量实时临床数据的可用性需要新的方法来充分利用这些数据进行实时患者风险预测。简化数据以适用于传统方法或做出不符合观察真实动态的不切实际假设并不理想。我们展示了一种协调需要机械通气预测的横断面和纵向信息的初步努力。
