The global COVID-19 pandemic has caused more than six million deaths worldwide. Medicalized hotels were established in Taiwan as quarantine facilities for COVID-19 patients with no or mild symptoms. Due to limited medical care available at these hotels, it is of paramount importance to identify patients at risk of clinical deterioration. This study aimed to develop and evaluate a graph-based deep learning approach for progressive hospital transfer risk prediction in a medicalized hotel setting. Vital sign measurements were obtained for 632 patients and daily patient similarity graphs were constructed. Inductive graph convolutional network models were trained on top of the temporally integrated graphs to predict hospital transfer risk. The proposed models achieved AUC scores above 0.83 for hospital transfer risk prediction based on the measurements of past 1, 2, and 3 days, outperforming baseline machine learning methods. A post-hoc analysis on the constructed diffusion-based graph using Local Clustering Coefficient discovered a high-risk cluster with significantly older mean age, higher body temperature, lower SpO2, and shorter length of stay. Further time-to-hospital-transfer survival analysis also revealed a significant decrease in survival probability in the discovered high-risk cluster. The obtained results demonstrated promising predictability and interpretability of the proposed graph-based approach. This technique may help preemptively detect high-risk patients at community-based medical facilities similar to a medicalized hotel.
翻译:全球COVID-19大流行已造成全世界600多万人死亡,在台湾建立了医疗旅馆,作为COVID-19病人的检疫设施,无症状或轻微症状;由于这些旅馆提供的医疗服务有限,至关重要的是查明有临床恶化风险的病人;这项研究旨在开发和评价基于图表的深入学习方法,以便在医疗化的旅馆环境中逐步进行医院转移风险预测;为632名病人和每天病人的类似图制作了生命标志测量;在时间综合图表的基础上,对进化图传播网络模型进行了培训,以预测医院转移风险;拟议的模型在根据过去1、2天和3天的测量结果,在医院转移风险预测方面达到0.83奥地利统一分类的分数,超过了基线机学方法;对利用地方集群节能技术对基于建筑的传播图进行的一项事后分析发现了一个高风险群,其平均年龄大大老化,体温较高,SpOO2更低,逗留时间较短;进一步进行时间到医院转移的网络模型分析,以预测医院转移生存风险风险;在所发现的高风险区级医院前的医保率方法方面,取得了有希望的可预测性,这是对高风险的医学风险前诊断。