ArticleAnnals of translational medicine2021
A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 8 of them syntheses that pooled it.
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Who cites it
52 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19.The Cochrane database of systematic reviews · 2022Pooled it
- Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review.International journal of environmental research and public health · 2021Pooled it
- Early prediction keys for COVID-19 cases progression: A meta-analysis.Journal of infection and public health · 2021Pooled it
- Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19.The Cochrane database of systematic reviews · 2021Pooled it
- Routine laboratory testing to determine if a patient has COVID-19.The Cochrane database of systematic reviews · 2020Pooled it
- Signs and symptoms to determine if a patient presenting in primary care or hospital outpatient settings has COVID-19 disease.The Cochrane database of systematic reviews · 2020Pooled it
- Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisalBMJ (Clinical research ed.) · 2020Pooled it
- Pooled it
- Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning.BMC medical informatics and decision making · 2025Article
- Comprehensive review on COVID-19: etiology, pathogenicity, and treatment.Frontiers in medicine · 2025Review
- The possibilities of prospective assessment of SARS-CoV-2 infection risk based on an artificial neural network model - a cross-sectional andArchives of medical science : AMS · 2025Article
- Novel insight into prediction model for sleep quality among college students: a LASSO-derived sleep evaluation.Frontiers in psychiatry · 2025Article
- Artificial Intelligence in the Intensive Care Unit: Current Evidence on an Inevitable Future Tool.Cureus · 2024Review
- Application of multi-gene genetic programming to the prognosis prediction of COVID-19 using routine hematological variables.Scientific reports · 2024Article
- A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques.Diagnostics (Basel, Switzerland) · 2023Review
- Severe/critical COVID-19 early warning system based on machine learning algorithms using novel imaging scores.World journal of clinical cases · 2023Article
- Equilibrium-based COVID-19 diagnosis from routine blood tests: A sparse deep convolutional model.Expert systems with applications · 2023Article
- Conventional and Novel Diagnostic Tools for the Diagnosis of Emerging SARS-CoV-2 Variants.Vaccines · 2023Review
- Clinical characteristics and outcome of Covid-19 illness and predictors of in-hospital mortality in Saudi Arabia.BMC infectious diseases · 2022Article
- Prognostic Model of ICU Admission Risk in Patients with COVID-19 Infection Using Machine Learning.Diagnostics (Basel, Switzerland) · 2022Article
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23 authors.
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Abstract
backgroundCurrently, the need to prevent and control the spread of the 2019 novel coronavirus disease (COVID-19) outside of Hubei province in China and internationally has become increasingly critical. We developed and validated a diagnostic model that does not rely on computed tomography (CT) images to aid in the early identification of suspected COVID-19 pneumonia (S-COVID-19-P) patients admitted to adult fever clinics and made the validated model available via an online triage calculator.
methodsPatients admitted from January 14 to February 26, 2020 with an epidemiological history of exposure to COVID-19 were included in the study [model development group (n=132) and validation group (n=32)]. Candidate features included clinical symptoms, routine laboratory tests, and other clinical information on admission. The features selection and model development were based on the least absolute shrinkage and selection operator (LASSO) regression. The primary outcome was the development and validation of a diagnostic aid model for the early identification of S-COVID-19-P on admission.
resultsThe development cohort contained 26 cases of S-COVID-19-P and seven cases of confirmed COVID-19 pneumonia (C-COVID-19-P). The final selected features included one demographic variable, four vital signs, five routine blood values, seven clinical signs and symptoms, and one infection-related biomarker. The model's performance in the testing set and the validation group resulted in area under the receiver operating characteristic (ROC) curves (AUCs) of 0.841 and 0.938, F1 scores of 0.571 and 0.667, recall of 1.000 and 1.000, specificity of 0.727 and 0.778, and precision of 0.400 and 0.500, respectively. The top five most important features were age, interleukin-6 (IL-6), systolic blood pressure (SYS_BP), monocyte ratio (MONO%), and fever classification (FC). Based on this model, an optimized strategy for the early identification of S-COVID-19-P in fever clinics has also been designed.
conclusionsA machine-learning model based solely on clinical information and not on CT images was able to perform the early identification of S-COVID-19-P on admission in fever clinics with a 100% recall score. This high-performing and validated model has been deployed as an online triage tool, which is available at https://intensivecare.shinyapps.io/COVID19/.
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