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](https://asset.jmir.pub/assets/691306f54326652e2f06865d41f30e63.png 480w,https://asset.jmir.pub/assets/691306f54326652e2f06865d41f30e63.png 960w,https://asset.jmir.pub/assets/691306f54326652e2f06865d41f30e63.png 1920w,https://asset.jmir.pub/assets/691306f54326652e2f06865d41f30e63.png 2500w)
Journals
- Islam M, Poly T, Alsinglawi B, Lin M, Hsu M, Li Y. A State-of-the-Art Survey on Artificial Intelligence to Fight COVID-19. Journal of Clinical Medicine 2021;10(9):1961 View
- Pang B, Li H, Liu Q, Wu P, Xia T, Zhang X, Le W, Li J, Lai L, Ou C, Ma J, Liu S, Zhou F, Wang X, Xie J, Zhang Q, Jiang M, Liu Y, Zeng Q. CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study. Frontiers in Medicine 2021;8 View
- Guo Y, Zhang Y, Lyu T, Prosperi M, Wang F, Xu H, Bian J. The application of artificial intelligence and data integration in COVID-19 studies: a scoping review. Journal of the American Medical Informatics Association 2021;28(9):2050 View
- Laino M, Ammirabile A, Posa A, Cancian P, Shalaby S, Savevski V, Neri E. The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review. Diagnostics 2021;11(8):1317 View
- Matsumoto T, Walston S, Walston M, Kabata D, Miki Y, Shiba M, Ueda D. Deep Learning–Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs. Journal of Digital Imaging 2022;36(1):178 View
- Laino M, Ammirabile A, Lofino L, Lundon D, Chiti A, Francone M, Savevski V. Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence. Emergency Radiology 2022;29(2):243 View
- Chieregato M, Frangiamore F, Morassi M, Baresi C, Nici S, Bassetti C, Bnà C, Galelli M. A hybrid machine learning/deep learning COVID-19 severity predictive model from CT images and clinical data. Scientific Reports 2022;12(1) View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Hasan M, Koo I. Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals. Diagnostics 2023;13(14):2358 View
- 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
- 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
- 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
- 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
- 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
- Pyo J, Chau N, Park E, Choi S. Computed tomography-based imaging biomarker identifies coal workers’ pneumoconiosis. Frontiers in Physiology 2023;14 View
- 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
- 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
Books/Policy Documents
- Tran V, To T, Nguyen T, Tran T. Intelligence of Things: Technologies and Applications. View
- Nguyen T, Tran L, Cuong P, Tran T. Proceedings of the Third International Conference on Sustainable Civil Engineering and Architecture. View
- 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