Prognosis patients with COVID-19 using deep learning

• Published in: BMC medical informatics and decision making Vol. 22; no. 1; pp. 78 - 18
• Main Authors: Guadiana-Alvarez, José Luis, Hussain, Fida, Morales-Menendez, Ruben, Rojas-Flores, Etna, García-Zendejas, Arturo, Escobar, Carlos A., Ramírez-Mendoza, Ricardo A., Wang, Jianhong
• Format: Journal Article
• Language: English
• Published: London BioMed Central 26.03.2022; BioMed Central Ltd; Springer Nature B.V; BMC
• Subjects: Aged; Algorithms; Area Under Curve; Biomarkers; Coronavirus; Coronaviruses; COVID-19; Datasets; Deep Learning; Female; Health Informatics; Hospitals; Humans; Information Systems and Communication Service; Machine learning; Male; Management of Computing and Information Systems; Management systems; Medical care; Medical prognosis; Medicine; Medicine & Public Health; Mortality; Mortality risk; Mortality risk prediction; Pandemics; Patients; Prognosis; Quality management; Random forest; Resource allocation; Risk assessment; Risk factors (Health); Sampling; Sampling methods; Viral diseases; Viruses; Mexico; Spain; Deep learning; COVID-19; Mortality risk prediction; Random forest; Coronavirus; Prognosis
• ISSN: 1472-6947; 1472-6947
• DOI: 10.1186/s12911-022-01820-x

Background The coronavirus (COVID-19) is a novel pandemic and recently we do not have enough knowledge about the virus behaviour and key performance indicators (KPIs) to assess the mortality risk forecast. However, using a lot of complex and expensive biomarkers could be impossible for many low budget hospitals. Timely identification of the risk of mortality of COVID-19 patients (RMCPs) is essential to improve hospitals' management systems and resource allocation standards. Methods For the mortality risk prediction, this research work proposes a COVID-19 mortality risk calculator based on a deep learning (DL) model and based on a dataset provided by the HM Hospitals Madrid, Spain. A pre-processing strategy for unbalanced classes and feature selection is proposed. To evaluate the proposed methods, an over-sampling Synthetic Minority TEchnique (SMOTE) and data imputation approaches are introduced which is based on the K-nearest neighbour. Results A total of 1,503 seriously ill COVID-19 patients having a median age of 70 years old are comprised in the research work, with 927 (61.7%) males and 576 (38.3%) females. A total of 48 features are considered to evaluate the proposed method, and the following results are achieved. It includes the following values i.e., area under the curve (AUC) 0.93, F2 score 0.93, recall 1.00, accuracy, 0.95, precision 0.91, specificity 0.9279 and maximum probability of correct decision (MPCD) 0.93. Conclusion The results show that the proposed method is significantly best for the mortality risk prediction of patients with COVID-19 infection. The MPCD score shows that the proposed DL outperforms on every dataset when evaluating even with an over-sampling technique. The benefits of the data imputation algorithm for unavailable biomarker data are also evaluated. Based on the results, the proposed scheme could be an appropriate tool for critically ill Covid-19 patients to assess the risk of mortality and prognosis.