This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2171) Machine Learning (1056) Clinical Information and Decision Making (951) Decision Support for Health Professionals (772) Outbreak and Pandemic Preparedness and Management (1494) Artificial Intelligence (717) Infectious Diseases (non-STD/STI) (1255) Theme Issue: Medical Informatics and COVID-19 (98)

Published on in Vol 23 , No 2 (2021) :February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23458, first published .
Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study

Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study

Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study

Authors of this article:

Kenji Ikemura 1, 2 Author Orcid Image ;   Eran Bellin 3 Author Orcid Image ;   Yukako Yagi 4 Author Orcid Image ;   Henny Billett 5 Author Orcid Image ;   Mahmoud Saada 2 Author Orcid Image ;   Katelyn Simone 2 Author Orcid Image ;   Lindsay Stahl 3 Author Orcid Image ;   James Szymanski 1 Author Orcid Image ;   D Y Goldstein 1 Author Orcid Image ;   Morayma Reyes Gil 1 Author Orcid Image
Article Authors Cited by (34) Tweetations (53) Metrics

Journals

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  21. Varelas G, Sakkopoulos E, Tzimas G, Phillips-Wren G, Mora M, Wang F, Gomez J. Exploring the role of country social and medical characteristics in patient level mortality in COVID-19 pandemic using Unsupervised Learning. Intelligent Decision Technologies 2022;16(1):231 View
  22. de Paiva B, Pereira P, de Andrade C, Gomes V, Souza-Silva M, Martins K, Sales T, de Carvalho R, Pires M, Ramos L, Silva R, de Freitas Martins Vieira A, Nunes A, de Oliveira Jorge A, de Oliveira Maurílio A, Scotton A, da Silva C, Cimini C, Ponce D, Pereira E, Manenti E, Rodrigues F, Anschau F, Botoni F, Bartolazzi F, Grizende G, Noal H, Duani H, Gomes I, Costa J, di Sabatino Santos Guimarães J, Tupinambás J, Rugolo J, Batista J, de Alvarenga J, Chatkin J, Ruschel K, Zandoná L, Pinheiro L, Menezes L, de Oliveira L, Kopittke L, Assis L, Marques L, Raposo M, Floriani M, Bicalho M, Nogueira M, de Oliveira N, Ziegelmann P, Paraiso P, de Lima Martelli P, Senger R, Menezes R, Francisco S, Araújo S, Kurtz T, Fereguetti T, de Oliveira T, Ribeiro Y, Ramires Y, Lima M, Carneiro M, Bezerra A, Schwarzbold A, de Moura Costa A, Farace B, Silveira D, de Almeida Cenci E, Lucas F, Aranha F, Bastos G, Vietta G, Nascimento G, Vianna H, Guimarães H, de Morais J, Moreira L, de Oliveira L, de Deus Sousa L, de Souza Viana L, de Souza Cabral M, Ferreira M, de Godoy M, de Figueiredo M, Guimarães-Junior M, de Paula de Sordi M, da Cunha Severino Sampaio N, Assaf P, Lutkmeier R, Valacio R, Finger R, de Freitas R, Guimarães S, Oliveira T, Diniz T, Gonçalves M, Marcolino M. Potential and limitations of machine meta-learning (ensemble) methods for predicting COVID-19 mortality in a large inhospital Brazilian dataset. Scientific Reports 2023;13(1) View
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  25. Yang F, Qiao Y, Qi Y, Bo J, Wang X. BACS: blockchain and AutoML-based technology for efficient credit scoring classification. Annals of Operations Research 2022 View
  26. Syed A, Khan T, Alromema N. A Hybrid Feature Selection Approach to Screen a Novel Set of Blood Biomarkers for Early COVID-19 Mortality Prediction. Diagnostics 2022;12(7):1604 View
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Books/Policy Documents

  1. Blagojević A, Geroski T. Applied Artificial Intelligence: Medicine, Biology, Chemistry, Financial, Games, Engineering. View