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

Clinical Information and Decision Making (1328) JMIR Theme Issue: COVID-19 Special Issue (2391) Machine Learning (1385) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1482) Outbreak and Pandemic Preparedness and Management (1675) Infectious Diseases (non-STD/STI) (1485) Artificial Intelligence (1198) Decision Support for Health Professionals (1137)

Published on in Vol 23, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23948, first published .
A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation

A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation

A Multimodality Machine Learning Approach to Differentiate Severe and Nonsevere COVID-19: Model Development and Validation

Authors of this article:

Yuanfang Chen1, 2 Author Orcid Image ;   Liu Ouyang3 Author Orcid Image ;   Forrest S Bao4 Author Orcid Image ;   Qian Li5 Author Orcid Image ;   Lei Han1, 6 Author Orcid Image ;   Hengdong Zhang1, 6 Author Orcid Image ;   Baoli Zhu1, 6, 7 Author Orcid Image ;   Yaorong Ge8 Author Orcid Image ;   Patrick Robinson9 Author Orcid Image ;   Ming Xu1, 6, 9 Author Orcid Image ;   Jie Liu10 Author Orcid Image ;   Shi Chen9, 11 Author Orcid Image
Article Authors Cited by (27) Tweetations (6) Metrics

Journals

  1. Pezoulas V, Kourou K, Papaloukas C, Triantafyllia V, Lampropoulou V, Siouti E, Papadaki M, Salagianni M, Koukaki E, Rovina N, Koutsoukou A, Andreakos E, Fotiadis D. A Multimodal Approach for the Risk Prediction of Intensive Care and Mortality in Patients with COVID-19. Diagnostics 2021;12(1):56 View
  2. Liu T, Siegel E, Shen D. Deep Learning and Medical Image Analysis for COVID-19 Diagnosis and Prediction. Annual Review of Biomedical Engineering 2022;24(1):179 View
  3. 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
  4. Matysek A, Studnicka A, Smith W, Hutny M, Gajewski P, Filipiak K, Goh J, Yang G. Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population. Frontiers in Medicine 2022;9 View
  5. Parviz M, Brieghel C, Agius R, Niemann C. Prediction of clinical outcome in CLL based on recurrent gene mutations, CLL-IPI variables, and (para)clinical data. Blood Advances 2022;6(12):3716 View
  6. Moslehi S, Mahjub H, Farhadian M, Soltanian A, Mamani M. Interpretable generalized neural additive models for mortality prediction of COVID-19 hospitalized patients in Hamadan, Iran. BMC Medical Research Methodology 2022;22(1) View
  7. Tarasova O, Biziukova N, Shemshura A, Filimonov D, Kireev D, Pokrovskaya A, Poroikov V. Identification of Molecular Mechanisms Involved in Viral Infection Progression Based on Text Mining: Case Study for HIV Infection. International Journal of Molecular Sciences 2023;24(2):1465 View
  8. Zhang H, Zhong F, Wang B, Liao M. A Nomogram Predicting the Severity of COVID-19 Based on Initial Clinical and Radiologic Characteristics. Future Virology 2022;17(4):221 View
  9. Tulu T, Wan T, Chan C, Wu C, Woo P, Tseng C, Vodencarevic A, Menni C, Chan K. Machine learning-based prediction of COVID-19 mortality using immunological and metabolic biomarkers. BMC Digital Health 2023;1(1) View
  10. Mustafa A. Mohammad R, Aljabri M, Aboulnour M, Mirza S, Alshobaiki A, Ramachandran M. Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques. Applied Computational Intelligence and Soft Computing 2022;2022:1 View
  11. Pezoulas V, Kourou K, Mylona E, Papaloukas C, Liontos A, Biros D, Milionis O, Kyriakopoulos C, Kostikas K, Milionis H, Fotiadis D. ICU admission and mortality classifiers for COVID-19 patients based on subgroups of dynamically associated profiles across multiple timepoints. Computers in Biology and Medicine 2022;141:105176 View
  12. Monday H, Li J, Nneji G, Nahar S, Hossin M, Jackson J, Ejiyi C. COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network. Diagnostics 2022;12(3):741 View
  13. Xiong Y, Ma Y, Ruan L, Li D, Lu C, Huang L. Comparing different machine learning techniques for predicting COVID-19 severity. Infectious Diseases of Poverty 2022;11(1) View
  14. Ahmadinejad N, Ayyoubzadeh S, Zeinalkhani F, Delazar S, Javanmard Z, Ahmadinejad Z, Mohajeri A, Esmaeili M. Discovering associations between radiological features and COVID‐19 patients' deterioration. Health Science Reports 2023;6(9) View
  15. Tang S, Chen T, Kuo K, Huang J, Kuo C, Chu Y. Using artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study. Journal of the Chinese Medical Association 2023;86(11):1020 View
  16. Alahmadi A, Alansari A, Alsheikh N, Alshammasi S, Alshamery M, Al-abdulmohsin R, Al Rabia L, Al Nass F, Alghamdi M, Almustafa S, Aljamea Z, Kurdi S, Islam M, Hussein D. Beta blockers may be protective in COVID-19; findings of a study to develop an interpretable machine learning model to assess COVID-19 disease severity in light of clinical findings, medication history, and patient comorbidities. Informatics in Medicine Unlocked 2023;42:101341 View
  17. Salvi M, Loh H, Seoni S, Barua P, García S, Molinari F, Acharya U. Multi-modality approaches for medical support systems: A systematic review of the last decade. Information Fusion 2024;103:102134 View
  18. Chadaga K, Prabhu S, Sampathila N, Chadaga R. Severity prediction in COVID-19 patients using clinical markers and explainable artificial intelligence: A stacked ensemble machine learning approach. Intelligent Decision Technologies 2023;17(4):959 View
  19. Ghaderzadeh M, Asadi F, Ramezan Ghorbani N, Almasi S, Taami T. Toward artificial intelligence (AI) applications in the determination of COVID-19 infection severity: considering AI as a disease control strategy in future pandemics. Iranian Journal of Blood and Cancer 2023;15(3):93 View
  20. Liu P, Xing Z, Peng X, Zhang M, Shu C, Wang C, Li R, Tang L, Wei H, Ran X, Qiu S, Gao N, Yeo Y, Liu X, Ji F. Machine learning versus multivariate logistic regression for predicting severe COVID‐19 in hospitalized children with Omicron variant infection. Journal of Medical Virology 2024;96(2) View
  21. 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
  22. Li C, Wu K, Yang R, Liao M, Li J, Zhu Q, Zhang J, Zhang X. Comprehensive analysis of immunogenic cell death-related gene and construction of prediction model based on WGCNA and multiple machine learning in severe COVID-19. Scientific Reports 2024;14(1) View

Books/Policy Documents

  1. Shaban-Nejad A, Michalowski M, Bianco S. Multimodal AI in Healthcare. View
  2. Badiola-Zabala G, Lopez-Guede J, Estevez J, Graña M. Bio-inspired Systems and Applications: from Robotics to Ambient Intelligence. View