a novel artificial intelligence assisted triage tool to aid in the diagnosis of suspected CORD-Papers-2021-10-25 (Version 1)

Title: A novel artificial intelligence-assisted triage tool to aid in the diagnosis of suspected COVID-19 pneumonia cases in fever clinics
Abstract: Background: Currently, 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 Methods: Patients 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 Results: The 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 models 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 Conclusions: A 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/
Published: 2021
Journal: Annals of Translational Medicine
Author Name: Feng, C
Author link: https://covid19-data.nist.gov/pid/rest/local/author/feng_c
Author Name: Wang, L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_l
Author Name: Chen, X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chen_x
Author Name: Zhai, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhai_y
Author Name: Zhu, F
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhu_f
Author Name: Chen, H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chen_h
Author Name: Wang, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_y
Author Name: Su, X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/su_x
Author Name: Huang, S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/huang_s
Author Name: Tian, L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/tian_l
Author Name: Zhu, W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhu_w
Author Name: Sun, W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/sun_w
Author Name: Zhang, L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_l
Author Name: Han, Q
Author link: https://covid19-data.nist.gov/pid/rest/local/author/han_q
Author Name: Zhang, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_j
Author Name: Pan, F
Author link: https://covid19-data.nist.gov/pid/rest/local/author/pan_f
Author Name: Chen, L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chen_l
Author Name: Zhu, Z
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhu_z
Author Name: Xiao, H
Author link: https://covid19-data.nist.gov/pid/rest/local/author/xiao_h
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Liu, G
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_g
Author Name: Chen, W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chen_w
Author Name: Li, T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/li_t
license: unk
license_url: [unknown license]
source_x: WHO
source_x_url: https://www.who.int/
who_covidence_id: #1110874
has_full_text: FALSE
G_ID: a_novel_artificial_intelligence_assisted_triage_tool_to_aid_in_the_diagnosis_of_suspected
S2 ID: 232207012