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

JMIR Theme Issue: COVID-19 Special Issue (2494) Computer-Aided Diagnosis (182) Machine Learning (1738) Outbreak and Pandemic Preparedness and Management (1780) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1531)

Published on in Vol 23, No 1 (2021): January

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25535, first published .
Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach

Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach

Authors of this article:

Ming Xu1, 2, 3 Author Orcid Image ;   Liu Ouyang4 Author Orcid Image ;   Lei Han1, 2 Author Orcid Image ;   Kai Sun5 Author Orcid Image ;   Tingting Yu6 Author Orcid Image ;   Qian Li7 Author Orcid Image ;   Hua Tian8 Author Orcid Image ;   Lida Safarnejad9, 10 Author Orcid Image ;   Hengdong Zhang1, 2 Author Orcid Image ;   Yue Gao1 Author Orcid Image ;   Forrest Sheng Bao11 Author Orcid Image ;   Yuanfang Chen12 Author Orcid Image ;   Patrick Robinson3 Author Orcid Image ;   Yaorong Ge10 Author Orcid Image ;   Baoli Zhu2 Author Orcid Image ;   Jie Liu13 Author Orcid Image ;   Shi Chen3, 14 Author Orcid Image
Article Authors Cited by (36) Tweetations (67) Metrics

Journals

  1. 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
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  4. Kline A, Wang H, Li Y, Dennis S, Hutch M, Xu Z, Wang F, Cheng F, Luo Y. Multimodal machine learning in precision health: A scoping review. npj Digital Medicine 2022;5(1) View
  5. Stahlschmidt S, Ulfenborg B, Synnergren J. Multimodal deep learning for biomedical data fusion: a review. Briefings in Bioinformatics 2022;23(2) View
  6. Ahmed Z, Renart E, Zeeshan S. Investigating underlying human immunity genes, implicated diseases and their relationship to COVID-19. Personalized Medicine 2022;19(3):229 View
  7. Mohsen F, Ali H, El Hajj N, Shah Z. Artificial intelligence-based methods for fusion of electronic health records and imaging data. Scientific Reports 2022;12(1) View
  8. Guo X, Zhang Y, Lu S, Lu Z. A Survey on Machine Learning in COVID-19 Diagnosis. Computer Modeling in Engineering & Sciences 2022;130(1):23 View
  9. Nwanosike E, Conway B, Merchant H, Hasan S. Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review. International Journal of Medical Informatics 2022;159:104679 View
  10. Barua A, Ahmed M, Begum S. A Systematic Literature Review on Multimodal Machine Learning: Applications, Challenges, Gaps and Future Directions. IEEE Access 2023;11:14804 View
  11. 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
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  13. Althenayan A, AlSalamah S, Aly S, Nouh T, Mirza A. Detection and Classification of COVID-19 by Radiological Imaging Modalities Using Deep Learning Techniques: A Literature Review. Applied Sciences 2022;12(20):10535 View
  14. Ayesha S, Hanif M, Talib R. Performance Enhancement of Predictive Analytics for Health Informatics Using Dimensionality Reduction Techniques and Fusion Frameworks. IEEE Access 2022;10:753 View
  15. 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
  16. Lasker A, Obaidullah S, Chakraborty C, Roy K. Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review. SN Computer Science 2022;4(1) View
  17. Alduaiji N, Algarni A, Abdalaha Hamza S, Abdel Azim G, Hamam H. A Lightweight CNN and Class Weight Balancing on Chest X-ray Images for COVID-19 Detection. Electronics 2022;11(23):4008 View
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  19. Wen R, Xu P, Cai Y, Wang F, Li M, Zeng X, Liu C. A Deep Learning Model for the Diagnosis and Discrimination of Gram-Positive and Gram-Negative Bacterial Pneumonia for Children Using Chest Radiography Images and Clinical Information. Infection and Drug Resistance 2023;Volume 16:4083 View
  20. Badnjević A, Pokvić L, Smajlhodžić-Deljo M, Spahić L, Bego T, Meseldžić N, Prnjavorac L, Prnjavorac B, Bedak O. Application of artificial intelligence for the classification of the clinical outcome and therapy in patients with viral infections: The case of COVID-19. Technology and Health Care 2024;32(3):1859 View
  21. Pinhata J, Brandao A, Leite D, Oliveira R, Fukasawa L, Gonçalves M, Guerra J, Araujo L, Mansueli G, Santos L, Borghesan T, Kimura L, Takahashi J, Garcia J, Piza A, Ferreira C, Polatto R, Guerra M, Fazioli R, Zanella R, Blanco R. Rapid response of a public health reference laboratory to the COVID-19 pandemic. Journal of Medical Microbiology 2023;72(10) View
  22. Pravin S, Rohith G, V K, Saranya J, Latha B, Vigneshwar K, Krishna S, Nambirajan H, Sumitra Y. PixNet for early diagnosis of COVID-19 using CT images. Multimedia Tools and Applications 2024;84(10):8179 View
  23. Khalili H, Wimmer M. Towards Improved XAI-Based Epidemiological Research into the Next Potential Pandemic. Life 2024;14(7):783 View
  24. 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
  25. Teoh J, Dong J, Zuo X, Lai K, Hasikin K, Wu X. Advancing healthcare through multimodal data fusion: a comprehensive review of techniques and applications. PeerJ Computer Science 2024;10:e2298 View
  26. Xu Z, Scharp D, Hobensack M, Ye J, Zou J, Ding S, Shang J, Topaz M. Machine learning-based infection diagnostic and prognostic models in post-acute care settings: a systematic review. Journal of the American Medical Informatics Association 2025;32(1):241 View
  27. Borazjani K, Khosravan N, Ying L, Hosseinalipour S. Multi-Modal Federated Learning for Cancer Staging Over Non-IID Datasets With Unbalanced Modalities. IEEE Transactions on Medical Imaging 2025;44(1):556 View
  28. Li X, Peng L, Wang Y, Zhang W. Open challenges and opportunities in federated foundation models towards biomedical healthcare. BioData Mining 2025;18(1) View
  29. Wu J, He K, Mao R, Shang X, Cambria E. Harnessing the potential of multimodal EHR data: A comprehensive survey of clinical predictive modeling for intelligent healthcare. Information Fusion 2025:103283 View

Books/Policy Documents

  1. Awotunde J, Jimoh R, Ogundokun R, Misra S, Abikoye O. Artificial Intelligence for Cloud and Edge Computing. View
  2. Leung C, Fung D, Hoi C. Big Data Analytics and Knowledge Discovery. View

Conference Proceedings

  1. Leung C, Daniel Mai T, Thong Tran N, Zhang C. 2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE). Predictive Analytics to Support Health Informatics on COVID-19 Data View
  2. Leung C, Madill E, Tran N, Zhang C. 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). Health Informatics on Big COVID-19 Pandemic Data via N-Shot Learning View
  3. Leung C, Fung D, Daniel Mai T, Souza J, Thong Tran N. 2021 IEEE International Conference on Digital Health (ICDH). A Digital Health System for Disease Analytics View
  4. Leung C, Fung D, Mai D, Wen Q, Tran J, Souza J. 25th International Database Engineering & Applications Symposium. Explainable Data Analytics for Disease and Healthcare Informatics View
  5. Zaman M, Fatima T, Hameed S, Haider S, Akhunzada A, Azeem M. 2024 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT). Enhanced Pediatric Pneumonia Diagnosis with Chest X-Ray using Deep Attention Mechanism View