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10.1038/s41746-021-00446-z http://scihub22266oqcxt.onion/10.1038/s41746-021-00446-z 33888856!8062628!33888856     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       NPJ+Digit+Med 2021 ; 4 (1): 75 Nephropedia Template TP {{tp|p=33888856|t=2021. AI-based analysis of CT images for rapid triage of COVID-19 patients |pdf=|usr=}}{{33888856}} gab.com Text AI-based analysis of CT images for rapid triage of COVID-19 patients JO: NPJ Digit Med http://www.kidney.de/pget.php?&tw=1&pmid=33888856 #FOAvip The COVID-19 pandemic overwhelms the medical resources in the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). We performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from nine external hospitals, achieved satisfying performance for predicting ICU, MV, and death of COVID-19 patients (AUROC 0.916, 0.919, and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943, and 0.856). Both clinical and image features showed complementary roles in prediction and provided accurate estimates to the time of progression (p < 0.001). Our findings are valuable for optimizing the use of medical resources in the COVID-19 pandemic. The models are available here: https://github.com/terryli710/COVID_19_Rapid_Triage_Risk_Predictor . Twit Text FOAVip AI-based analysis of CT images for rapid triage of COVID-19 patients ????NPJ Digit Med http://www.kidney.de/pget.php?&tw=1&pmid=33888856 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33888856 #FOAvip English Wikipedia <ref>{{Cite pmid|33888856}}</ref> AI-based analysis of CT images for rapid triage of COVID-19 patients #MMPMID33888856 Xu Q; Zhan X; Zhou Z; Li Y; Xie P; Zhang S; Li X; Yu Y; Zhou C; Zhang L; Gevaert O; Lu G NPJ Digit Med 2021[Apr]; 4 (1): 75 PMID33888856show ga The COVID-19 pandemic overwhelms the medical resources in the stressed intensive care unit (ICU) capacity and the shortage of mechanical ventilation (MV). We performed CT-based analysis combined with electronic health records and clinical laboratory results on Cohort 1 (n = 1662 from 17 hospitals) with prognostic estimation for the rapid stratification of PCR confirmed COVID-19 patients. These models, validated on Cohort 2 (n = 700) and Cohort 3 (n = 662) constructed from nine external hospitals, achieved satisfying performance for predicting ICU, MV, and death of COVID-19 patients (AUROC 0.916, 0.919, and 0.853), even on events happened two days later after admission (AUROC 0.919, 0.943, and 0.856). Both clinical and image features showed complementary roles in prediction and provided accurate estimates to the time of progression (p < 0.001). Our findings are valuable for optimizing the use of medical resources in the COVID-19 pandemic. The models are available here: https://github.com/terryli710/COVID_19_Rapid_Triage_Risk_Predictor . äAI-based analysis of CT images for rapid triage of COVID-19 patients (epmc).pdf DeepDyve Pubget Overpricing