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

JMIR Theme Issue: COVID-19 Special Issue (2388) Machine Learning (1370) Outbreak and Pandemic Preparedness and Management (1673) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1481) Clinical Information and Decision Making (1314) Infectious Diseases (non-STD/STI) (1478)

Published on in Vol 23, No 5 (2021): May

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25988, first published .
Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study

Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study

Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study

Authors of this article:

Julián Benito-León1 Author Orcid Image ;   Mª Dolores del Castillo2 Author Orcid Image ;   Alberto Estirado3 Author Orcid Image ;   Ritwik Ghosh4 Author Orcid Image ;   Souvik Dubey5 Author Orcid Image ;   J Ignacio Serrano2 Author Orcid Image
Article Authors Cited by (17) Tweetations (4) Metrics

Journals

  1. Romero J, Bravo-Martín A, Oliva-Navarrete P, Sánchez-Cuesta F, Ríos-Lago M, Benito-León J. Impact of COVID-19 on a brain damage unit. Diabetes & Metabolic Syndrome: Clinical Research & Reviews 2021;15(4):102163 View
  2. Sun X, Zhou C, Zhu J, Wu S, Liang T, Jiang J, Chen J, Chen T, Huang S, Chen L, Ye Z, Guo H, Zhan X, Liu C. Identification of clinical heterogeneity and construction of a novel subtype predictive model in patients with ankylosing spondylitis: An unsupervised machine learning study. International Immunopharmacology 2023;117:109879 View
  3. Sonnweber T, Tymoszuk P, Sahanic S, Boehm A, Pizzini A, Luger A, Schwabl C, Nairz M, Grubwieser P, Kurz K, Koppelstätter S, Aichner M, Puchner B, Egger A, Hoermann G, Wöll E, Weiss G, Widmann G, Tancevski I, Löffler-Ragg J. Investigating phenotypes of pulmonary COVID-19 recovery: A longitudinal observational prospective multicenter trial. eLife 2022;11 View
  4. Banoei M, Dinparastisaleh R, Zadeh A, Mirsaeidi M. Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying. Critical Care 2021;25(1) View
  5. Trivedi S, Patra P, Singh A, Deka P, Srivastava P. Analyzing the research trends of COVID-19 using topic modeling approach. Journal of Modelling in Management 2023;18(4):1204 View
  6. Ilbeigipour S, Albadvi A, Akhondzadeh Noughabi E. Cluster-based analysis of COVID-19 cases using self-organizing map neural network and K-means methods to improve medical decision-making. Informatics in Medicine Unlocked 2022;32:101005 View
  7. Zhou C, Huang S, Liang T, Jiang J, Chen J, Chen T, Chen L, Sun X, Zhu J, Wu S, Ye Z, Guo H, Chen W, Liu C, Zhan X. Machine learning-based clustering in cervical spondylotic myelopathy patients to identify heterogeneous clinical characteristics. Frontiers in Surgery 2022;9 View
  8. Zhang L, Wei J, Wei J, Zhang Z, Zhang J, Tang Q, Wang Y, Pan Y, Qin X. Identification of Clinical Heterogeneity and Construction of Prediction Models for Novel Subtypes in Patients with Abdominal Aortic Aneurysm: An Unsupervised Machine Learning Study. Annals of Vascular Surgery 2024;98:75 View
  9. Huang C, Xu H. Individual-level precision diagnosis for coronavirus disease 2019 related severe outcome: an early study in New York. Scientific Reports 2023;13(1) View
  10. Yao Y, Wu S, Liu C, Zhou C, Zhu J, Chen T, Huang C, Feng S, Zhang B, Wu S, Ma F, Liu L, Zhan X. Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning. Annals of Medicine 2023;55(2) View
  11. Pisano F, Cannas B, Fanni A, Pasella M, Canetto B, Giglio S, Mocci S, Chessa L, Perra A, Littera R. Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19. Frontiers in Medicine 2023;10 View
  12. Balaji T, Bablani A, Sreeja S, Misra H. Sensecor: A framework for COVID-19 variants severity classification and symptoms detection. Evolving Systems 2023 View
  13. Deng J, Zhou C, Xiao F, Chen J, Li C, Xie Y. Construction of a predictive model for blood transfusion in patients undergoing total hip arthroplasty and identification of clinical heterogeneity. Scientific Reports 2024;14(1) View
  14. Badiola-Zabala G, Lopez-Guede J, Estevez J, Graña M. Machine Learning First Response to COVID-19: A Systematic Literature Review of Clinical Decision Assistance Approaches during Pandemic Years from 2020 to 2022. Electronics 2024;13(6):1005 View
  15. Santos-Silva M, Sousa N, Sousa J. Artificial intelligence in routine blood tests. Frontiers in Medical Engineering 2024;2 View
  16. Fernández D, Perez-Alvarez N, Molist G, Chang D. COVID-19 patient profiles over four waves in Barcelona metropolitan area: A clustering approach. PLOS ONE 2024;19(5):e0302461 View

Books/Policy Documents

  1. Badiola-Zabala G, Lopez-Guede J, Estevez J, Graña M. Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. View