Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm.
Abstract: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected c...
| Publicado en: | Medicine Vol. 100; no. 24; pp. 1 - 10 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Lippincott Williams & Wilkins
6/18/2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151531675&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151531675 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00257974 2S6 jtl: Medicine issn: 00257974 maglogo: N pubinfo: dt: 6/18/2021 vid: 100 iid: 24 pid: 433 pub: Lippincott Williams & Wilkins place: Baltimore, Maryland artinfo: ui: 151531675 151531675 NLM34128861 10.1097/MD.0000000000026279 NLM34128861 151531675 ppf: 1 ppct: 9 formats: tig: atl: Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm. aug: au: Suxia Bao Hong-yi Pan Wei Zheng Qing-Qing Wu Yi-Ning Dai Nan-Nan Sun Tian-Chen Hui Wen-Hao Wu Yi-Cheng Huang Guo-Bo Chen Qiao-Qiao Yin Li-Juan Wu Rong Yan Ming-Shan Wang Mei-Juan Chen Jia-Jie Zhang Li-Xia Yu Ji-Chan Shi Nian Fang Yue-Fei Shen affil: Department of Infectious Diseases, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou 310014 sug: ab: Abstract: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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