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

JMIR Theme Issue: COVID-19 Special Issue (2452) Artificial Intelligence (1371) Machine Learning (1465) Outbreak and Pandemic Preparedness and Management (1727) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517) Clinical Information and Decision Making (1407) Infectious Diseases (non-STD/STI) (1575) Decision Support for Health Professionals (1185) Telehealth and Telemonitoring (1671)

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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27060, first published .
Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation

Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation

Prediction and Feature Importance Analysis for Severity of COVID-19 in South Korea Using Artificial Intelligence: Model Development and Validation

Authors of this article:

Heewon Chung1 Author Orcid Image ;   Hoon Ko1 Author Orcid Image ;   Wu Seong Kang2 Author Orcid Image ;   Kyung Won Kim3 Author Orcid Image ;   Hooseok Lee1 Author Orcid Image ;   Chul Park4 Author Orcid Image ;   Hyun-Ok Song5 Author Orcid Image ;   Tae-Young Choi6 Author Orcid Image ;   Jae Ho Seo7 Author Orcid Image ;   Jinseok Lee1 Author Orcid Image
Article Authors Cited by (26) Tweetations (4) Metrics

Journals

  1. Saleh M, AlHamaydeh M, Zakaria M. Shear capacity prediction for reinforced concrete deep beams with web openings using artificial intelligence methods. Engineering Structures 2023;280:115675 View
  2. Gonçalves A, de Araujo G, Garcia L, Amaral F, Schneider I. Feature Importance Analysis by Nowcasting Perspective to Predict COVID-19. Mobile Networks and Applications 2022;27(5):1967 View
  3. Neves da Silva L, Domingues Fernandes R, Costa R, Oliveira A, Sá A, Mosca A, Oliveira B, Braga M, Mendes M, Carvalho A, Moreira P, Santa Cruz A. Prediction of Noninvasive Ventilation Failure in COVID-19 Patients: When Shall We Stop?. Cureus 2022 View
  4. Lau Y, Dulebenets M, Yip H, Tang Y. Healthcare Supply Chain Management under COVID-19 Settings: The Existing Practices in Hong Kong and the United States. Healthcare 2022;10(8):1549 View
  5. Wong K, Xiang Y, Yin L, So H. Uncovering Clinical Risk Factors and Predicting Severe COVID-19 Cases Using UK Biobank Data: Machine Learning Approach. JMIR Public Health and Surveillance 2021;7(9):e29544 View
  6. Doyle R. Machine Learning–Based Prediction of COVID-19 Mortality With Limited Attributes to Expedite Patient Prognosis and Triage: Retrospective Observational Study. JMIRx Med 2021;2(4):e29392 View
  7. Altini N, Brunetti A, Mazzoleni S, Moncelli F, Zagaria I, Prencipe B, Lorusso E, Buonamico E, Carpagnano G, Bavaro D, Poliseno M, Saracino A, Schirinzi A, Laterza R, Di Serio F, D’Introno A, Pesce F, Bevilacqua V. Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters. Sensors 2021;21(24):8503 View
  8. Tolmachev I, Kaverina I, Vrazhnov D, Starikov I, Starikova E, Kostuchenko E. Application of Artificial Intelligence Methods Depending on the Tasks Solved during COVID-19 Pandemic. COVID 2022;2(10):1341 View
  9. Demko I, Korchagin E, Cherkashin O, Gordeeva N, Anikin D, Anikina D. Possibilities of information systems for prediction of outcomes of new coronavirus infection COVID-19. Meditsinskiy sovet = Medical Council 2022;(4):42 View
  10. Chung H, Park C, Kang W, Lee J. Gender Bias in Artificial Intelligence: Severity Prediction at an Early Stage of COVID-19. Frontiers in Physiology 2021;12 View
  11. Bartoszko J, Dranitsaris G, Wilcox M, Del Sorbo L, Mehta S, Peer M, Parotto M, Bogoch I, Riazi S. Development of a repeated-measures predictive model and clinical risk score for mortality in ventilated COVID-19 patients. Canadian Journal of Anesthesia/Journal canadien d'anesthésie 2022;69(3):343 View
  12. Paul S, Saha A, Biswas A, Zulfiker M, Arefin M, Rahman M, Reza A. Combating Covid-19 using machine learning and deep learning: Applications, challenges, and future perspectives. Array 2023;17:100271 View
  13. Chowdhury N, Kabir M, Rahman M, Islam S. Machine learning for detecting COVID-19 from cough sounds: An ensemble-based MCDM method. Computers in Biology and Medicine 2022;145:105405 View
  14. Ng A, Wei B, Jain J, Ward E, Tandon S, Moskowitz J, Krogh-Jespersen S, Wakschlag L, Alshurafa N. Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation. JMIR mHealth and uHealth 2022;10(8):e33850 View
  15. Hosseinzadeh Kasani P, Lee J, Park C, Yun C, Jang J, Lee S. Evaluation of nutritional status and clinical depression classification using an explainable machine learning method. Frontiers in Nutrition 2023;10 View
  16. Shi J, Bendig D, Vollmar H, Rasche P. Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study. Journal of Medical Internet Research 2023;25:e45815 View
  17. Hida M, Imai R, Nakamura M, Nakao H, Kitagawa K, Wada C, Eto S, Takeda M, Imaoka M. Investigation of factors influencing low physical activity levels in community-dwelling older adults with chronic pain: a cross-sectional study. Scientific Reports 2023;13(1) View
  18. Zhao Y, Chen Z, Jian X. A High-Generalizability Machine Learning Framework for Analyzing the Homogenized Properties of Short Fiber-Reinforced Polymer Composites. Polymers 2023;15(19):3962 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. Kim J, Choi Y, Lee Y, Yeo S, Kim K, Kim M, Rahmati M, Yon D, Lee J. Limitations of the Cough Sound-Based COVID-19 Diagnosis Artificial Intelligence Model and its Future Direction: Longitudinal Observation Study. Journal of Medical Internet Research 2024;26:e51640 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. Zhou H, Ma L, Niu X, Xiang Y, Chen J, Su Y, Li J, Lu S, Chen C, Wu Q. A novel hybrid model combined with ensemble embedded feature selection method for estimating reference evapotranspiration in the North China Plain. Agricultural Water Management 2024;296:108807 View
  23. Zan J, Dong X, Yang H, Yan J, He Z, Tian J, Zhang Y. Application of the Unbalanced Ensemble Algorithm for Prognostic Prediction Outcomes of All-Cause Mortality in Coronary Heart Disease Patients Comorbid with Hypertension. Risk Management and Healthcare Policy 2024;Volume 17:1921 View
  24. Li P, Yang H, Wu J, Ma Y, Hou A, Chen J, Ning N. Development and validation of predictive models for mortality of cases with COVID-19 (Omicron BA.5.2.48 and B.7.14): a retrospective study. BMJ Open 2024;14(10):e082616 View
  25. Kong W, Liu Y, Li W, Yang K, Yu L, Jiao G. Correlation between oxygenation function and laboratory indicators in COVID-19 patients based on non-enhanced chest CT images and construction of an artificial intelligence prediction model. Frontiers in Microbiology 2024;15 View

Books/Policy Documents

  1. Badiola-Zabala G, Lopez-Guede J, Estevez J, Graña M. Hybrid Artificial Intelligent Systems. View