School of Artificial IntelligenceGuilin University of Electronic Technology Guilin Guanxgi 541004 China.
2Laboratory for Imagery, Vision, and Artificial IntelligenceEcole de technologie Superieure Montreal QC H3C 1K3 Canada.
IEEE J Transl Eng Health Med. 2023 Mar 14;11:223-231. doi: 10.1109/JTEHM.2023.3256966. eCollection 2023.
Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients.
We have considered two scenarios for survival analysis, 1) uni-variate analysis using the Log-rank test and Kaplan-Meier estimator and 2) combining all clinical variables ([Formula: see text]=44) for predicting the short-term from long-term survival. We considered the random forest (RF) model as a baseline model, comparing to our proposed 1D CNN in predicting survival groups.
Our experiments using the univariate analysis show that nine clinical variables are significantly associated with the survival outcome with corrected p < 0.05. Our approach of 1D CNN shows a significant improvement in performance metrics compared to the RF and the state-of-the-art techniques (i.e., 1D CNN) in predicting the survival group of patients with COVID-19.
Our model has been tested using clinical variables, where the performance is found promising. The 1D CNN model could be a useful tool for detecting the risk of mortality and developing treatment plans in a timely manner.
The findings indicate that using both Heparin and Exnox for treatment is typically the most useful factor in predicting a patient's chances of survival from COVID-19. Moreover, our predictive model shows that the combination of AI and clinical data can be applied to point-of-care services through fast-learning healthcare systems.
数以百万计的人受到 2019 年冠状病毒病（COVID-19）的影响，这在全球范围内造成了数百万人死亡。人工智能（AI）在患者护理的各个领域（包括预后）中发挥着越来越重要的作用。本文提出了一种基于一维卷积神经网络（1D CNN）的新型预测模型，用于使用临床变量预测 COVID-19 患者的生存结果。
我们考虑了生存分析的两种情况，1）使用对数秩检验和 Kaplan-Meier 估计器的单变量分析，2）结合所有临床变量（[公式：见文本]=44）预测短期和长期生存。我们将随机森林（RF）模型作为基线模型，与我们提出的用于预测生存组的 1D CNN 进行比较。
我们使用单变量分析的实验表明，有九个临床变量与生存结果显著相关，校正后 p<0.05。与 RF 和最先进的技术（即 1D CNN）相比，我们的 1D CNN 方法在预测 COVID-19 患者的生存组方面表现出显著的性能提升。
我们的模型已经使用临床变量进行了测试，结果很有前途。1D CNN 模型可以成为及时检测死亡率风险和制定治疗计划的有用工具。
研究结果表明，使用肝素和 Exnox 进行治疗通常是预测患者 COVID-19 生存机会最有用的因素。此外，我们的预测模型表明，通过快速学习的医疗保健系统，可以将人工智能和临床数据的结合应用于护理点服务。
