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

Theme Issue: Medical Informatics and COVID-19 (107) Artificial Intelligence (1377) Clinical Information and Decision Making (1411) JMIR Theme Issue: COVID-19 Special Issue (2453) Decision Support for Health Professionals (1191) Imaging Informatics (194) Machine Learning (1471) Infectious Diseases (non-STD/STI) (1576) Outbreak and Pandemic Preparedness and Management (1728)

Published on in Vol 9, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24973, first published .
Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study

Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study

Deep Learning Models for Predicting Severe Progression in COVID-19-Infected Patients: Retrospective Study

Authors of this article:

Thao Thi Ho1 Author Orcid Image ;   Jongmin Park2 Author Orcid Image ;   Taewoo Kim1 Author Orcid Image ;   Byunggeon Park2 Author Orcid Image ;   Jaehee Lee3 Author Orcid Image ;   Jin Young Kim4 Author Orcid Image ;   Ki Beom Kim5 Author Orcid Image ;   Sooyoung Choi6 Author Orcid Image ;   Young Hwan Kim7 Author Orcid Image ;   Jae-Kwang Lim2 Author Orcid Image ;   Sanghun Choi1 Author Orcid Image
Article Authors Cited by (27) Tweetations (2) Metrics

Journals

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  8. Ortiz S, Rojas F, Valenzuela O, Herrera L, Rojas I. Determination of the Severity and Percentage of COVID-19 Infection through a Hierarchical Deep Learning System. Journal of Personalized Medicine 2022;12(4):535 View
  9. Miller J, Tada M, Goto M, Chen H, Dang E, Mohr N, Lee S. Prediction models for severe manifestations and mortality due to COVID‐19: A systematic review. Academic Emergency Medicine 2022;29(2):206 View
  10. Wynants L, Van Calster B, Collins G, Riley R, Heinze G, Schuit E, Albu E, Arshi B, Bellou V, Bonten M, Dahly D, Damen J, Debray T, de Jong V, De Vos M, Dhiman P, Ensor J, Gao S, Haller M, Harhay M, Henckaerts L, Heus P, Hoogland J, Hudda M, Jenniskens K, Kammer M, Kreuzberger N, Lohmann A, Levis B, Luijken K, Ma J, Martin G, McLernon D, Navarro C, Reitsma J, Sergeant J, Shi C, Skoetz N, Smits L, Snell K, Sperrin M, Spijker R, Steyerberg E, Takada T, Tzoulaki I, van Kuijk S, van Bussel B, van der Horst I, Reeve K, van Royen F, Verbakel J, Wallisch C, Wilkinson J, Wolff R, Hooft L, Moons K, van Smeden M. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ 2020:m1328 View
  11. Tabatabaie M, Sarrami A, Didehdar M, Tasorian B, Shafaat O, Sotoudeh H. Accuracy of Machine Learning Models to Predict Mortality in COVID-19 Infection Using the Clinical and Laboratory Data at the Time of Admission. Cureus 2021 View
  12. Park S, Tran V, Lee D. Application of Various YOLO Models for Computer Vision-Based Real-Time Pothole Detection. Applied Sciences 2021;11(23):11229 View
  13. Rehman M, Shafique A, Khan K, Khalid S, Alotaibi A, Althobaiti T, Ramzan N, Ahmad J, Shah S, Abbasi Q. Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model. Sensors 2022;22(2):461 View
  14. Yao L, Dong W, Wan J, Howard S, Li M, Graff J. Graphical Trajectory Comparison to Identify Errors in Data of COVID-19: A Cross-Country Analysis. Journal of Personalized Medicine 2021;11(10):955 View
  15. Hasan M, Koo I. Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals. Diagnostics 2023;13(14):2358 View
  16. Verzellesi L, Botti A, Bertolini M, Trojani V, Carlini G, Nitrosi A, Monelli F, Besutti G, Castellani G, Remondini D, Milanese G, Croci S, Sverzellati N, Salvarani C, Iori M. Machine and Deep Learning Algorithms for COVID-19 Mortality Prediction Using Clinical and Radiomic Features. Electronics 2023;12(18):3878 View
  17. Wang C, Liu S, Tang Y, Yang H, Liu J. Diagnostic Test Accuracy of Deep Learning Prediction Models on COVID-19 Severity: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2023;25:e46340 View
  18. Schaudt D, von Schwerin R, Hafner A, Riedel P, Reichert M, von Schwerin M, Beer M, Kloth C. Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset. Scientific Reports 2023;13(1) 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. Tehrani S, Zarvani M, Amiri P, Ghods Z, Raoufi M, Safavi-Naini S, Soheili A, Gharib M, Abbasi H. Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data. BMC Medical Informatics and Decision Making 2023;23(1) View
  21. Pyo J, Chau N, Park E, Choi S. Computed tomography-based imaging biomarker identifies coal workers’ pneumoconiosis. Frontiers in Physiology 2023;14 View
  22. Ferrigno I, Verzellesi L, Ottone M, Bonacini M, Rossi A, Besutti G, Bonelli E, Colla R, Facciolongo N, Teopompi E, Massari M, Mancuso P, Ferrari A, Pattacini P, Trojani V, Bertolini M, Botti A, Zerbini A, Giorgi Rossi P, Iori M, Salvarani C, Croci S. CCL18, CHI3L1, ANG2, IL-6 systemic levels are associated with the extent of lung damage and radiomic features in SARS-CoV-2 infection. Inflammation Research 2024;73(4):515 View
  23. Chaudhari C, Fegade S, Gantayat S, Jugnu K, Sawan V. Influenza Diagnosis Deep Learning: Machine Learning Approach for Pharyngeal Image Infection. EAI Endorsed Transactions on Pervasive Health and Technology 2024;10 View
  24. Yousefi S, Saen R, Shabanpour H, Ghods K. An innovative patient clustering method using data envelopment Analysis–Discriminant analysis and artificial neural networks: A case study in healthcare systems. Socio-Economic Planning Sciences 2024;95:102054 View

Books/Policy Documents

  1. Tran V, To T, Nguyen T, Tran T. Intelligence of Things: Technologies and Applications. View
  2. Nguyen T, Tran L, Cuong P, Tran T. Proceedings of the Third International Conference on Sustainable Civil Engineering and Architecture. View
  3. Nguyen Q, Doan V, Tran T, Nguyen T. Proceedings of the 7th International Conference on Geotechnics, Civil Engineering and Structures, CIGOS 2024, 4-5 April, Ho Chi Minh City, Vietnam. View