The mortality risk factors for coronavirus disease (COVID-19) must be early predicted, especially for severe cases, to provide intensive care before they develop to critically ill immediately. This paper aims to develop an optimized convolution neural network (CNN) for predicting mortality risk factors for COVID-19 patients. The proposed model supports two types of input data clinical variables and the computed tomography (CT) scans. The features are extracted from the optimized CNN phase and then applied to the classification phase. The CNN model's hyperparameters were optimized using a proposed genetic-based adaptive momentum estimation (GB-ADAM) algorithm. The GB-ADAM algorithm employs the genetic algorithm (GA) to optimize Adam optimizer's configuration parameters, consequently improving the classification accuracy. The model is validated using three recent cohorts from New York, Mexico, and Wuhan, consisting of 3055, 7497,504 patients, respectively. The results indicated that the most significant mortality risk factors are: CD T Lymphocyte (Count), D-dimer greater than 1 Ug/ml, high values of lactate dehydrogenase (LDH), C-reactive protein (CRP), hypertension, and diabetes. Early identification of these factors would help the clinicians in providing immediate care. The results also show that the most frequent COVID-19 signs in CT scans included ground-glass opacity (GGO), followed by crazy-paving pattern, consolidations, and the number of lobes. Moreover, the experimental results show encouraging performance for the proposed model compared with different predicting models.

中文翻译：

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使用深度学习预测 COVID-19 患者死亡风险因素的基于遗传的自适应动量估计

必须及早预测冠状病毒病 (COVID-19) 的死亡风险因素，特别是对于重症病例，以便在他们立即发展为重症之前提供重症监护。本文旨在开发一种优化的卷积神经网络 (CNN)，用于预测 COVID-19 患者的死亡风险因素。所提出的模型支持两种类型的输入数据临床变量和计算机断层扫描 (CT) 扫描。从优化的 CNN 阶段提取特征，然后应用于分类阶段。CNN 模型的超参数使用提出的基于遗传的自适应动量估计 (GB-ADAM) 算法进行了优化。GB-ADAM 算法采用遗传算法 (GA) 来优化 Adam 优化器的配置参数，从而提高分类精度。该模型使用来自纽约、墨西哥和武汉的三个最近的队列进行验证，分别由 3055、7497,504 名患者组成。结果表明，最显着的死亡风险因素是：CDT 淋巴细胞（计数）、D-二聚体大于 1 Ug/ml、乳酸脱氢酶 (LDH)、C 反应蛋白 (CRP)、高血压和糖尿病的高值。早期识别这些因素将有助于临床医生提供即时护理。结果还表明，CT 扫描中最常见的 COVID-19 体征包括磨玻璃影 (GGO)，其次是疯狂铺路图案、实变和肺叶数量。此外，实验结果表明，与不同的预测模型相比，该模型的性能令人鼓舞。