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Analysis of model performance on the hold-out CRD dataset a..:
Lakshya Singhal (11478546)
;
Yash Garg (11478549)
;
Philip Yang (11478552)
...
https://figshare.com/articles/dataset/Analysis_of_model_performance_on_the_hold-out_CRD_dataset_across_demographics_/16678929. , 2021
Link:
https://doi.org/10.1371/journal.pone.0257056.t004
RT Journal T1
Analysis of model performance on the hold-out CRD dataset across demographics
UL https://suche.suub.uni-bremen.de/peid=base-ftsmithonian:oai:figshare.com:article_16678929&Exemplar=1&LAN=DE A1 Lakshya Singhal (11478546) A1 Yash Garg (11478549) A1 Philip Yang (11478552) A1 Azade Tabaie (11436346) A1 A. Ian Wong (11478555) A1 Akram Mohammed (3477728) A1 Lokesh Chinthala (11478558) A1 Dipen Kadaria (11478561) A1 Amik Sodhi (11478564) A1 Andre L. Holder (11478567) A1 Annette Esper (6385298) A1 James M. Blum (7300496) A1 Robert L. Davis (11478570) A1 Gari D. Clifford (6508535) A1 Greg S. Martin (9155149) A1 Rishikesan Kamaleswaran (10176173) YR 2021 K1 Cell Biology K1 Genetics K1 Science Policy K1 Mental Health K1 Biological Sciences not elsewhere classified K1 Information Systems not elsewhere classified K1 systolic blood pressure K1 performed significantly better K1 maximum respiratory rate K1 machine learning algorithm K1 intensive care units K1 generate predictive models K1 electronic medical record K1 94 ]) K1 compared K1 92 &# 8211 K1 19 emr database K1 88 – 0 K1 75 – 0 K1 085 – 0 K1 identified salient features K1 18 &# 8211 K1 younger age group K1 older age group K1 div >< p K1 81 &# 8211 K1 comprising 899 covid K1 90 ) K1 sensitivity K1 19 </ p K1 81 – 0 K1 predicting covid19 patients K1 berlin clinical criteria K1 berlin criteria K1 19 patients K1 sbp ) K1 important features K1 clinical data K1 18 years K1 804 patients K1 predicting ards K1 site covid K1 observational window K1 moderate groups K1 identify risk K1 female groups K1 emory healthcare K1 data collected K1 center validation JF https://figshare.com/articles/dataset/Analysis_of_model_performance_on_the_hold-out_CRD_dataset_across_demographics_/16678929 LK http://dx.doi.org/https://doi.org/10.1371/journal.pone.0257056.t004 DO https://doi.org/10.1371/journal.pone.0257056.t004 SF ELIB - SuUB Bremen
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