Abstract
AbstractWe aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system.
Funder
Gastroenterology and Liver Diseases Research Centre of Shahid Beheshti University of Medical Sciences
Publisher
Springer Science and Business Media LLC
Reference39 articles.
1. Our World in Data. Daily New Confirmed COVID-19 Cases and Deaths Per Million People. https://ourworldindata.org/explorers/coronavirus-data-explorer?uniformYAxis=0&Interval=7-day+rolling+average&Relative+to+Population=true&country=USA~AUS~ITA~CAN~DEU~GBR~FRA&Metric=Cases+and+deaths&Color+by+test+positivity=false. Accessed 29 Aug 2022 (2022).
2. Majlesi, H. et al. Omicron variant of COVID-19: A focused review of biologic, clinical, and epidemiological changes. Immunopathol. Persa 9, e34449–e34449 (2022).
3. Girum, T., Lentiro, K., Geremew, M., Migora, B. & Shewamare, S. Global strategies and effectiveness for COVID-19 prevention through contact tracing, screening, quarantine, and isolation: A systematic review. Trop. Med. Health 48, 91. https://doi.org/10.1186/s41182-020-00285-w (2020).
4. Li, J. et al. Epidemiology of COVID-19: A systematic review and meta-analysis of clinical characteristics, risk factors, and outcomes. J. Med. Virol. 93, 1449–1458. https://doi.org/10.1002/jmv.26424 (2021).
5. Lalmuanawma, S., Hussain, J. & Chhakchhuak, L. Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos Solitons Fractals 139, 110059. https://doi.org/10.1016/j.chaos.2020.110059 (2020).
Cited by
11 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献