Nazir Amril, Ampadu Hyacinth Kwadwo
Department of Information Systems and Technology Management, College of Technological Innovation Zayed University, Abu Dhabi, United Arab Emirates.
Old Ahinsan, Kumasi-Ghana, Ghana.
PeerJ Comput Sci. 2022 Mar 17;8:e889. doi: 10.7717/peerj-cs.889. eCollection 2022.
The global healthcare system is being overburdened by an increasing number of COVID-19 patients. Physicians are having difficulty allocating resources and focusing their attention on high-risk patients, partly due to the difficulty in identifying high-risk patients early. COVID-19 hospitalizations require specialized treatment capabilities and can cause a burden on healthcare resources. Estimating future hospitalization of COVID-19 patients is, therefore, crucial to saving lives. In this paper, an interpretable deep learning model is developed to predict intensive care unit (ICU) admission and mortality of COVID-19 patients. The study comprised of patients from the Stony Brook University Hospital, with patient information such as demographics, comorbidities, symptoms, vital signs, and laboratory tests recorded. The top three predictors of ICU admission were ferritin, diarrhoea, and alamine aminotransferase, and the top predictors for mortality were COPD, ferritin, and myalgia. The proposed model predicted ICU admission with an AUC score of 88.3% and predicted mortality with an AUC score of 96.3%. The proposed model was evaluated against existing model in the literature which achieved an AUC of 72.8% in predicting ICU admission and achieved an AUC of 84.4% in predicting mortality. It can clearly be seen that the model proposed in this paper shows superiority over existing models. The proposed model has the potential to provide tools to frontline doctors to help classify patients in time-bound and resource-limited scenarios.
全球医疗系统正因越来越多的新冠病毒疾病（COVID-19）患者而不堪重负。医生在分配资源以及将注意力集中于高危患者方面存在困难，部分原因是难以早期识别高危患者。COVID-19住院患者需要专门的治疗能力，并且会给医疗资源带来负担。因此，预测COVID-19患者未来的住院情况对于挽救生命至关重要。在本文中，开发了一种可解释的深度学习模型来预测COVID-19患者的重症监护病房（ICU）收治情况和死亡率。该研究纳入了来自石溪大学医院的患者，并记录了患者的人口统计学信息、合并症、症状、生命体征和实验室检查等信息。ICU收治的前三大预测因素是铁蛋白、腹泻和谷丙转氨酶，而死亡率的前三大预测因素是慢性阻塞性肺疾病（COPD）、铁蛋白和肌痛。所提出的模型预测ICU收治情况的曲线下面积（AUC）得分为88.3%，预测死亡率的AUC得分为96.3%。将所提出的模型与文献中的现有模型进行评估，现有模型预测ICU收治情况的AUC为72.8%，预测死亡率的AUC为84.4%。可以清楚地看到，本文提出的模型比现有模型更具优势。所提出的模型有潜力为一线医生提供工具，以帮助在时间紧迫和资源有限的情况下对患者进行分类。
