Author: Andre Filipe de Moraes Batista; Joao Luiz Miraglia; Thiago Henrique Rizzi Donato; Alexandre Dias Porto Chiavegatto Filho
Title: COVID-19 diagnosis prediction in emergency care patients: a machine learning approach Document date: 2020_4_7
ID: nvavj9gk_20
Snippet: In conclusion, we found evidence to suggest that targeted decisions for receiving COVID-19 tests in areas with a shortage of supply is possible with the use of machine learning algorithms. For future clinical applications, locally training the algorithms in each specific healthcare service will be crucial to improve predictive performance. Annex 1 -Performance metrics for each machine learning algorithm for the 10-fold crossvalidation. . CC-BY-NC.....
Document: In conclusion, we found evidence to suggest that targeted decisions for receiving COVID-19 tests in areas with a shortage of supply is possible with the use of machine learning algorithms. For future clinical applications, locally training the algorithms in each specific healthcare service will be crucial to improve predictive performance. Annex 1 -Performance metrics for each machine learning algorithm for the 10-fold crossvalidation. . CC-BY-NC 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
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