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

Theme Issue: Medical Informatics and COVID-19 (104) JMIR Theme Issue: COVID-19 Special Issue (2394) Machine Learning (1401) Electronic Health Records (971) Clinical Information and Decision Making (1340) Decision Support for Health Professionals (1147) Imaging Informatics (187) Artificial Intelligence (1216) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1484)

Published on in Vol 9, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24572, first published .
Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study

Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study

Development and Validation of a Machine Learning Approach for Automated Severity Assessment of COVID-19 Based on Clinical and Imaging Data: Retrospective Study

Authors of this article:

Juan Carlos Quiroz1, 2 Author Orcid Image ;   You-Zhen Feng3 Author Orcid Image ;   Zhong-Yuan Cheng3 Author Orcid Image ;   Dana Rezazadegan1, 4 Author Orcid Image ;   Ping-Kang Chen3 Author Orcid Image ;   Qi-Ting Lin3 Author Orcid Image ;   Long Qian5 Author Orcid Image ;   Xiao-Fang Liu6, 7 Author Orcid Image ;   Shlomo Berkovsky1 Author Orcid Image ;   Enrico Coiera1 Author Orcid Image ;   Lei Song8 Author Orcid Image ;   Xiaoming Qiu9 Author Orcid Image ;   Sidong Liu1 Author Orcid Image ;   Xiang-Ran Cai3 Author Orcid Image
Article Authors Cited by (33) Tweetations (1) Metrics

Journals

  1. Kiebish M, Shah P, Sarangarajan R, Vishnudas V, Gesta S, Tekumalla P, Bountra C, Granger E, Schadt E, Rodrigues L, Narain N. Bayesian Model Infers Drug Repurposing Candidates for Treatment of COVID-19. Applied Sciences 2021;11(6):2466 View
  2. Lin B, Wu S. Digital Transformation in Personalized Medicine with Artificial Intelligence and the Internet of Medical Things. OMICS: A Journal of Integrative Biology 2022;26(2):77 View
  3. 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
  4. Moezzi M, Shirbandi K, Shahvandi H, Arjmand B, Rahim F. The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis. Informatics in Medicine Unlocked 2021;24:100591 View
  5. Snider B, Patel B, McBean E. Insights Into Co-Morbidity and Other Risk Factors Related to COVID-19 Within Ontario, Canada. Frontiers in Artificial Intelligence 2021;4 View
  6. 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
  7. Zhong T, Zhuang Z, Dong X, Wong K, Wong W, Wang J, He D, Liu S. Predicting Antituberculosis Drug–Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study. JMIR Medical Informatics 2021;9(7):e29226 View
  8. Gerotziafas G, Catalano M, Theodorou Y, Dreden P, Marechal V, Spyropoulos A, Carter C, Jabeen N, Harenberg J, Elalamy I, Falanga A, Fareed J, Agathaggelou P, Antic D, Antignani P, Bosch M, Brenner B, Chekhonin V, Colgan M, Dimopoulos M, Douketis J, Elnazar E, Farkas K, Fazeli B, Fowkes G, Gu Y, Gligorov J, Ligocki M, Indran T, Kannan M, Kantarcioglu B, Kasse A, Konstantinidis K, Leivano F, Lewis J, Makatsariya A, Mbaye P, Mahé I, Panovska-Stavridis I, Olinic D, Papageorgiou C, Pecsvarady Z, Pillon S, Ramacciotti E, Abdel-Razeq H, Sabbah M, Sassi M, Schernthaner G, Siddiqui F, Shiomura J, Slama-Schwok A, Wautrecht J, Tafur A, Taher A, Klein-Wegel P, Zhai Z, Zoubida T. The COVID-19 Pandemic and the Need for an Integrated and Equitable Approach: An International Expert Consensus Paper. Thrombosis and Haemostasis 2021;121(08):992 View
  9. Abdulaal A, Patel A, Al-Hindawi A, Charani E, Alqahtani S, Davies G, Mughal N, Moore L. Clinical Utility and Functionality of an Artificial Intelligence–Based App to Predict Mortality in COVID-19: Mixed Methods Analysis. JMIR Formative Research 2021;5(7):e27992 View
  10. Karthik R, Menaka R, Hariharan M, Won D. CT-based severity assessment for COVID-19 using weakly supervised non-local CNN. Applied Soft Computing 2022;121:108765 View
  11. Ağbulut Ü. Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms. Sustainable Production and Consumption 2022;29:141 View
  12. Butler L, Karabayir I, Samie Tootooni M, Afshar M, Goldberg A, Akbilgic O. Image and structured data analysis for prognostication of health outcomes in patients presenting to the ED during the COVID-19 pandemic. International Journal of Medical Informatics 2022;158:104662 View
  13. Park J, Norman G, Klasnja P, Rivera D, Hekler E. Development and Validation of Multivariable Prediction Algorithms to Estimate Future Walking Behavior in Adults: Retrospective Cohort Study. JMIR mHealth and uHealth 2023;11:e44296 View
  14. Saboury B, Morris M, Siegel E. Future Directions in Artificial Intelligence. Radiologic Clinics of North America 2021;59(6):1085 View
  15. Liu K, Ning X, Liu S. Medical Image Classification Based on Semi-Supervised Generative Adversarial Network and Pseudo-Labelling. Sensors 2022;22(24):9967 View
  16. Feng Y, Liu S, Cheng Z, Quiroz J, Rezazadegan D, Chen P, Lin Q, Qian L, Liu X, Berkovsky S, Coiera E, Song L, Qiu X, Cai X. Severity Assessment and Progression Prediction of COVID-19 Patients Based on the LesionEncoder Framework and Chest CT. Information 2021;12(11):471 View
  17. Hameed Abdulkareem K, Awad Mutlag A, Musa Dinar A, Frnda J, Abed Mohammed M, Hasan Zayr F, Lakhan A, Kadry S, Ali Khattak H, Nedoma J, Yahya A. Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments. Computational Intelligence and Neuroscience 2022;2022:1 View
  18. Coiera E, Liu S. Evidence synthesis, digital scribes, and translational challenges for artificial intelligence in healthcare. Cell Reports Medicine 2022;3(12):100860 View
  19. Abdulkareem K, Al-Mhiqani M, Dinar A, Mohammed M, Al-Imari M, Al-Waisy A, Alghawli A, Al-Qaness M. MEF: Multidimensional Examination Framework for Prioritization of COVID-19 Severe Patients and Promote Precision Medicine Based on Hybrid Multi-Criteria Decision-Making Approaches. Bioengineering 2022;9(9):457 View
  20. MUZOĞLU N, KARASLAN M, HALEFOĞLU A, YARMAN S. Prediction of the Prognosis of Covid-19 Disease Using Deep Learning Methods and Boruta Feature Selection Algorithm. Afyon Kocatepe University Journal of Sciences and Engineering 2022;22(3):577 View
  21. 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
  22. Enshaei N, Oikonomou A, Rafiee M, Afshar P, Heidarian S, Mohammadi A, Plataniotis K, Naderkhani F. COVID-rate: an automated framework for segmentation of COVID-19 lesions from chest CT images. Scientific Reports 2022;12(1) View
  23. 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
  24. Vinod D, Prabaharan S. Elucidation of infection asperity of CT scan images of COVID-19 positive cases: A Machine Learning perspective. Scientific African 2023;20:e01681 View
  25. Nguyen V, Duong X, Nguyen L, Nguyen P, Priya J, Truong T, Le H, Pham N, Nguyen X. An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO 2 emission. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 2023;45(3):9149 View
  26. 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
  27. Mansour M, Serbest K, Kutlu M, Cilli M. Estimation of lower limb joint moments based on the inverse dynamics approach: a comparison of machine learning algorithms for rapid estimation. Medical & Biological Engineering & Computing 2023;61(12):3253 View
  28. 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
  29. Nambiar A, S H, S S. Model-agnostic explainable artificial intelligence tools for severity prediction and symptom analysis on Indian COVID-19 data. Frontiers in Artificial Intelligence 2023;6 View
  30. 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

Books/Policy Documents

  1. Sigdel D, Steinecke D, Wang D, Liem D, Gupta M, Zhang A, Wang W, Ping P. Advanced Technologies in Cardiovascular Bioengineering. View