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

JMIR Theme Issue: COVID-19 Special Issue (2452) Machine Learning (1466) Clinical Information and Decision Making (1408) Decision Support for Health Professionals (1186) Outbreak and Pandemic Preparedness and Management (1728) Artificial Intelligence (1373) Infectious Diseases (non-STD/STI) (1575) Theme Issue: Medical Informatics and COVID-19 (107)

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 Ikemura1, 2 Author Orcid Image ;   Eran Bellin3 Author Orcid Image ;   Yukako Yagi4 Author Orcid Image ;   Henny Billett5 Author Orcid Image ;   Mahmoud Saada2 Author Orcid Image ;   Katelyn Simone2 Author Orcid Image ;   Lindsay Stahl3 Author Orcid Image ;   James Szymanski1 Author Orcid Image ;   D Y Goldstein1 Author Orcid Image ;   Morayma Reyes Gil1 Author Orcid Image
Article Authors Cited by (59) Tweetations (49) Metrics

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Yonis H, Yousif O, Yuliarto S, Zaaqoq A, Zabbe M, Zabert G, Zacharowski K, Zahid M, Zahran M, Zaidan N, Zambon M, Zambrano M, Zanella A, Zawadka K, Zaynah N, Zayyad H, Zoufaly A, Zucman D. At-admission prediction of mortality and pulmonary embolism in an international cohort of hospitalised patients with COVID-19 using statistical and machine learning methods. Scientific Reports 2024;14(1) View
  49. Bashkirova L, Kit I, Havryshchuk Y, Krasnova A, Vasylyuk-Zaitseva S. Artificial Intelligence in Medicine: from Diagnosis to Treatment. Futurity Medicine 2024;3(3) View
  50. Yuan H, Yu K, Xie F, Liu M, Sun S. Automated machine learning with interpretation: A systematic review of methodologies and applications in healthcare. Medicine Advances 2024;2(3):205 View
  51. Zhao Y, Li T, Yuan Q, Deng S. How to detect fake online physician reviews: A deep learning approach. DIGITAL HEALTH 2024;10 View
  52. Park Y. Automated machine learning with R: AutoML tools for beginners in clinical research. Journal of Minimally Invasive Surgery 2024;27(3):129 View
  53. An J, Kim I, Kim K, Park J, Kang H, Kim H, Kim Y, Ahn J. Efficacy of automated machine learning models and feature engineering for diagnosis of equivocal appendicitis using clinical and computed tomography findings. Scientific Reports 2024;14(1) View
  54. Kim T, Lee H. Evaluating AI Models and Predictors for COVID-19 Infection Dependent on Data from Patients with Cancer or Not: A Systematic Review. Korean Journal of Clinical Pharmacy 2024;34(3):141 View
  55. Caterson J, Lewin A, Williamson E. The application of explainable artificial intelligence (XAI) in electronic health record research: A scoping review. DIGITAL HEALTH 2024;10 View

Books/Policy Documents

  1. Blagojević A, Geroski T. Applied Artificial Intelligence: Medicine, Biology, Chemistry, Financial, Games, Engineering. View
  2. Siam N, Khan M, Rownak M, Juel M, Uddin A. Machine Intelligence and Emerging Technologies. View
  3. Dhote S, Roberts M, Sridhar K. Innovations in VLSI, Signal Processing and Computational Technologies. View