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

JMIR Theme Issue: COVID-19 Special Issue (2452) Infectious Diseases (non-STD/STI) (1575) Statistical Methods for Surveillance and Population Health (156) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1517)

Published on in Vol 22, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/20259, first published .
Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation

Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation

Prognostic Modeling of COVID-19 Using Artificial Intelligence in the United Kingdom: Model Development and Validation

Authors of this article:

Ahmed Abdulaal1 Author Orcid Image ;   Aatish Patel1 Author Orcid Image ;   Esmita Charani2 Author Orcid Image ;   Sarah Denny1 Author Orcid Image ;   Nabeela Mughal1 Author Orcid Image ;   Luke Moore1, 2 Author Orcid Image
Article Authors Cited by (66) Tweetations (16) Metrics

Journals

  1. Abdulaal A, Patel A, Charani E, Denny S, Alqahtani S, Davies G, Mughal N, Moore L. Comparison of deep learning with regression analysis in creating predictive models for SARS-CoV-2 outcomes. BMC Medical Informatics and Decision Making 2020;20(1) View
  2. Lorencin I, Baressi Šegota S, Anđelić N, Blagojević A, Šušteršić T, Protić A, Arsenijević M, Ćabov T, Filipović N, Car Z. Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks. Journal of Personalized Medicine 2021;11(1):28 View
  3. Pan P, Li Y, Xiao Y, Han B, Su L, Su M, Li Y, Zhang S, Jiang D, Chen X, Zhou F, Ma L, Bao P, Xie L. Prognostic Assessment of COVID-19 in the Intensive Care Unit by Machine Learning Methods: Model Development and Validation. Journal of Medical Internet Research 2020;22(11):e23128 View
  4. Ponsford M, Burton R, Smith L, Khan P, Andrews R, Cuff S, Tan L, Eberl M, Humphreys I, Babolhavaeji F, Artemiou A, Pandey M, Jolles S, Underwood J. Examining the utility of extended laboratory panel testing in the emergency department for risk stratification of patients with COVID-19: a single-centre retrospective service evaluation. Journal of Clinical Pathology 2022;75(4):255 View
  5. Jimenez-Solem E, Petersen T, Hansen C, Hansen C, Lioma C, Igel C, Boomsma W, Krause O, Lorenzen S, Selvan R, Petersen J, Nyeland M, Ankarfeldt M, Virenfeldt G, Winther-Jensen M, Linneberg A, Ghazi M, Detlefsen N, Lauritzen A, Smith A, de Bruijne M, Ibragimov B, Petersen J, Lillholm M, Middleton J, Mogensen S, Thorsen-Meyer H, Perner A, Helleberg M, Kaas-Hansen B, Bonde M, Bonde A, Pai A, Nielsen M, Sillesen M. Developing and validating COVID-19 adverse outcome risk prediction models from a bi-national European cohort of 5594 patients. Scientific Reports 2021;11(1) View
  6. Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Physica Medica 2021;83:194 View
  7. Kwon Y, Toussie D, Finkelstein M, Cedillo M, Maron S, Manna S, Voutsinas N, Eber C, Jacobi A, Bernheim A, Gupta Y, Chung M, Fayad Z, Glicksberg B, Oermann E, Costa A. Combining Initial Radiographs and Clinical Variables Improves Deep Learning Prognostication in Patients with COVID-19 from the Emergency Department. Radiology: Artificial Intelligence 2021;3(2):e200098 View
  8. Chee M, Ong M, Siddiqui F, Zhang Z, Lim S, Ho A, Liu N. Artificial Intelligence Applications for COVID-19 in Intensive Care and Emergency Settings: A Systematic Review. International Journal of Environmental Research and Public Health 2021;18(9):4749 View
  9. 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
  10. Adamidi E, Mitsis K, Nikita K. Artificial intelligence in clinical care amidst COVID-19 pandemic: A systematic review. Computational and Structural Biotechnology Journal 2021;19:2833 View
  11. Lan L, Sun W, Xu D, Yu M, Xiao F, Hu H, Xu H, Wang X. Artificial intelligence-based approaches for COVID-19 patient management. Intelligent Medicine 2021;1(1):10 View
  12. 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
  13. Snider B, McBean E, Yawney J, Gadsden S, Patel B. Identification of Variable Importance for Predictions of Mortality From COVID-19 Using AI Models for Ontario, Canada. Frontiers in Public Health 2021;9 View
  14. Stachel A, Keegan L, Blumberg S. Modeling transmission of pathogens in healthcare settings. Current Opinion in Infectious Diseases 2021;34(4):333 View
  15. Leite M, de Loiola Costa L, Cunha V, Kreniski V, de Oliveira Braga Filho M, da Cunha N, Costa F. Artificial intelligence and the future of life sciences. Drug Discovery Today 2021;26(11):2515 View
  16. Galanter W, Rodríguez-Fernández J, Chow K, Harford S, Kochendorfer K, Pishgar M, Theis J, Zulueta J, Darabi H. Predicting clinical outcomes among hospitalized COVID-19 patients using both local and published models. BMC Medical Informatics and Decision Making 2021;21(1) View
  17. Khozeimeh F, Sharifrazi D, Izadi N, Joloudari J, Shoeibi A, Alizadehsani R, Gorriz J, Hussain S, Sani Z, Moosaei H, Khosravi A, Nahavandi S, Islam S. Combining a convolutional neural network with autoencoders to predict the survival chance of COVID-19 patients. Scientific Reports 2021;11(1) View
  18. Sankaranarayanan S, Balan J, Walsh J, Wu Y, Minnich S, Piazza A, Osborne C, Oliver G, Lesko J, Bates K, Khezeli K, Block D, DiGuardo M, Kreuter J, O’Horo J, Kalantari J, Klee E, Salama M, Kipp B, Morice W, Jenkinson G. COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation. Journal of Medical Internet Research 2021;23(9):e30157 View
  19. Kuo K, Talley P, Chang C. The accuracy of machine learning approaches using non-image data for the prediction of COVID-19: A meta-analysis. International Journal of Medical Informatics 2022;164:104791 View
  20. Al-Hindawi A, Abdulaal A, Rawson T, Alqahtani S, Mughal N, Moore L. COVID-19 Prognostic Models: A Pro-con Debate for Machine Learning vs. Traditional Statistics. Frontiers in Digital Health 2021;3 View
  21. Ganjali R, Eslami S, Samimi T, Sargolzaei M, Firouraghi N, MohammadEbrahimi S, khoshrounejad F, Kheirdoust A. Clinical informatics solutions in COVID-19 pandemic: Scoping literature review. Informatics in Medicine Unlocked 2022;30:100929 View
  22. Raschke R, Rangan P, Agarwal S, Uppalapu S, Sher N, Curry S, Heise C, Wang Y. COVID-19 Time of Intubation Mortality Evaluation (C-TIME): A system for predicting mortality of patients with COVID-19 pneumonia at the time they require mechanical ventilation. PLOS ONE 2022;17(7):e0270193 View
  23. Kuno T, Sahashi Y, Kawahito S, Takahashi M, Iwagami M, Egorova N. Prediction of in‐hospital mortality with machine learning for COVID‐19 patients treated with steroid and remdesivir. Journal of Medical Virology 2022;94(3):958 View
  24. Chen J, Li Y, Guo L, Zhou X, Zhu Y, He Q, Han H, Feng Q. Machine learning techniques for CT imaging diagnosis of novel coronavirus pneumonia: a review. Neural Computing and Applications 2024;36(1):181 View
  25. Bottino F, Tagliente E, Pasquini L, Napoli A, Lucignani M, Figà-Talamanca L, Napolitano A. COVID Mortality Prediction with Machine Learning Methods: A Systematic Review and Critical Appraisal. Journal of Personalized Medicine 2021;11(9):893 View
  26. Singh V, Kamaleswaran R, Chalfin D, Buño-Soto A, San Roman J, Rojas-Kenney E, Molinaro R, von Sengbusch S, Hodjat P, Comaniciu D, Kamen A. A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers. iScience 2021;24(12):103523 View
  27. Becerra-Sánchez A, Rodarte-Rodríguez A, Escalante-García N, Olvera-González J, De la Rosa-Vargas J, Zepeda-Valles G, Velásquez-Martínez E. Mortality Analysis of Patients with COVID-19 in Mexico Based on Risk Factors Applying Machine Learning Techniques. Diagnostics 2022;12(6):1396 View
  28. Olivato M, Rossetti N, Gerevini A, Chiari M, Putelli L, Serina I. Machine Learning Models for Predicting Short-Long Length of Stay of COVID-19 Patients. Procedia Computer Science 2022;207:1232 View
  29. Segall R, Sankarasubbu V. Survey of Recent Applications of Artificial Intelligence for Detection and Analysis of COVID-19 and Other Infectious Diseases. International Journal of Artificial Intelligence and Machine Learning 2022;12(2):1 View
  30. Peng H, Hu C, Deng W, Huang L, Zhang Y, Luo B, Wang X, Long X, Huang X. Incubation period, clinical and lung CT features for early prediction of COVID-19 deterioration: development and internal verification of a risk model. BMC Pulmonary Medicine 2022;22(1) View
  31. Meng Z, Guo S, Zhou Y, Li M, Wang M, Ying B. Applications of laboratory findings in the prevention, diagnosis, treatment, and monitoring of COVID-19. Signal Transduction and Targeted Therapy 2021;6(1) View
  32. Bhatt P, Liu J, Gong Y, Wang J, Guo Y. Emerging Artificial Intelligence–Empowered mHealth: Scoping Review. JMIR mHealth and uHealth 2022;10(6):e35053 View
  33. Wu C, Wu M, Chen L, Lo Y, Huang C, Yu H, Pardeshi M, Lo W, Sheu R. AEP-DLA: Adverse Event Prediction in Hospitalized Adult Patients Using Deep Learning Algorithms. IEEE Access 2021;9:55673 View
  34. Nguyen S, Chan R, Cadena J, Soper B, Kiszka P, Womack L, Work M, Duggan J, Haller S, Hanrahan J, Kennedy D, Mukundan D, Ray P. Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients. Scientific Reports 2021;11(1) View
  35. Wang L, Zhang Y, Wang D, Tong X, Liu T, Zhang S, Huang J, Zhang L, Chen L, Fan H, Clarke M. Artificial Intelligence for COVID-19: A Systematic Review. Frontiers in Medicine 2021;8 View
  36. Cheng J, Sollee J, Hsieh C, Yue H, Vandal N, Shanahan J, Choi J, Tran T, Halsey K, Iheanacho F, Warren J, Ahmed A, Eickhoff C, Feldman M, Mortani Barbosa E, Kamel I, Lin C, Yi T, Healey T, Zhang P, Wu J, Atalay M, Bai H, Jiao Z, Wang J. COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data. European Radiology 2022;32(7):4446 View
  37. Ovcharenko E, Kutikhin A, Gruzdeva O, Kuzmina A, Slesareva T, Brusina E, Kudasheva S, Bondarenko T, Kuzmenko S, Osyaev N, Ivannikova N, Vavin G, Moses V, Danilov V, Komossky E, Klyshnikov K. Cardiovascular and Renal Comorbidities Included into Neural Networks Predict the Outcome in COVID-19 Patients Admitted to an Intensive Care Unit: Three-Center, Cross-Validation, Age- and Sex-Matched Study. Journal of Cardiovascular Development and Disease 2023;10(2):39 View
  38. Sarker S, Jamal L, Ahmed S, Irtisam N. Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. Robotics and Autonomous Systems 2021;146:103902 View
  39. Ramón A, Torres A, Milara J, Cascón J, Blasco P, Mateo J. eXtreme Gradient Boosting-based method to classify patients with COVID-19. Journal of Investigative Medicine 2022;70(7):1472 View
  40. Mano L, Torres A, Morales A, Cruz C, Cardoso F, Alves S, Faria C, Lanzillotti R, Cerceau R, da Costa R, Figueiredo K, Werneck V. Machine Learning Applied to COVID-19: A Review of the Initial Pandemic Period. International Journal of Computational Intelligence Systems 2023;16(1) View
  41. Ballaz S, Pulgar-Sánchez M, Chamorro K, Fernández-Moreira E. Scientific pertinence of developing machine learning technologies for the triage of COVID-19 patients: A bibliometric analysis via Scopus. Informatics in Medicine Unlocked 2023;41:101312 View
  42. 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
  43. Rangelov B, Young A, Lilaonitkul W, Aslani S, Taylor P, Guðmundsson E, Yang Q, Hu Y, Hurst J, Hawkes D, Jacob J, Bains P, Cushnan D, Halling-Brown M, Jefferson E, Lemarchand F, Sarellas A, Schofield D, Sutherland J, Watt M, Alexander D, Aziz H, Lewis E, Lip G, Manser P, Quinlan P, Sebire N, Swift A, Shetty S, Williams P, Bennett O, Dorgham S, Favaro A, Gan S, Ganepola T, Imreh G, Puri N, Rodrigues J, Oliver H, Hudson B, Robinson G, Wood R, Moreton A, Lomas K, Marchbank N, Law C, Chana H, Gandy N, Sharif B, Ismail L, Patel J, Wai D, Mathers L, Clark R, Harrar A, Bettany A, Foley K, Pothecary C, Buckle S, Roche L, Shah A, Kirkham F, Bown H, Seal S, Connoley H, Tugwell-Allsup J, Owen B, Jones M, Moth A, Colman J, Maskell G, Kim D, Sanchez-Cabello A, Lewis H, Thorley M, Kruger R, Chifu M, Ashley N, Spas S, Bates A, Halson P, Heafey C, McCann C, McCreavy D, Duvva D, Siah T, Deane J, Pearlman E, MacKay J, Sia M, Easter E, Brookes D, Burford P, Barbara R, Payne T, Ingram M, Bhatia B, Yusuf S, Rotherham F, Warren G, Heeney A, Bowen A, Wilson A, Hussain Z, Kellett J, Harrison R, Watkins J, Patterson L, Welsh T, Redwood D, Greig N, Van Pelt L, Palmer S, Milne K, Tilley J, Alexander M, Frary A, Babar J, Sadler T, Neil-Gallacher E, Cardona S, Gill A, Omeje N, Ridgeon C, Gleeson F, Johnstone A, Frood R, Rabani M, Scarsbrook A, Lyttle M, Lyen S, James G, Sheedy S, Homer K, Glover A, Gibbison B, Blazeby J, Baquedano M, Jacob T, Grubnic S, Crick T, Crawford D, Prestwood F, Cooper M, Radon M. Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes. Scientific Reports 2023;13(1) View
  44. 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
  45. Chen J, Lowin M, Kellner D, Hinz O, Adam E, Ippolito A, Wenger-Alakmeh K. Designing Expert-Augmented Clinical Decision Support Systems to Predict Mortality Risk in ICUs. KI - Künstliche Intelligenz 2023;37(2-4):227 View
  46. Casillas N, Ramón A, Torres A, Blasco P, Mateo J. Predictive Model for Mortality in Severe COVID-19 Patients across the Six Pandemic Waves. Viruses 2023;15(11):2184 View
  47. Charles V, Mousavi S, Gherman T, Mosavi S. From data to action: Empowering COVID-19 monitoring and forecasting with intelligent algorithms. Journal of the Operational Research Society 2024;75(7):1261 View
  48. Xin Y, Li H, Zhou Y, Yang Q, Mu W, Xiao H, Zhuo Z, Liu H, Wang H, Qu X, Wang C, Liu H, Yu K. The accuracy of artificial intelligence in predicting COVID-19 patient mortality: a systematic review and meta-analysis. BMC Medical Informatics and Decision Making 2023;23(1) View
  49. Giuste F, He L, Lais P, Shi W, Zhu Y, Hornback A, Tsai C, Isgut M, Anderson B, Wang M. Early and fair COVID-19 outcome risk assessment using robust feature selection. Scientific Reports 2023;13(1) View
  50. Ma F, He C, Yang H, Hu Z, Mao H, Fan C, Qi Y, Zhang J, Xu B. Interpretable machine-learning model for Predicting the Convalescent COVID-19 patients with pulmonary diffusing capacity impairment. BMC Medical Informatics and Decision Making 2023;23(1) View
  51. Chen R, Chen J, Yang S, Luo S, Xiao Z, Lu L, Liang B, Liu S, Shi H, Xu J. Prediction of prognosis in COVID-19 patients using machine learning: A systematic review and meta-analysis. International Journal of Medical Informatics 2023;177:105151 View
  52. Benjamin R. Reproduction number projection for the COVID-19 pandemic. Advances in Continuous and Discrete Models 2023;2023(1) View
  53. SAĞLAM E, SAVAŞ A, ÖKE D, ÖZLÜ C, KOÇAR B, ERKALP K. Intensive care unit: mortality score in early prediction of mortality in critical COVID-19 patients. Journal of Medicine and Palliative Care 2023;4(5):572 View
  54. Kumar N, Delu V, Ulasov I, Kumar S, Singh R, Kumar S, Shukla A, Patel A, Yadav L, Tiwari R, Rachana K, Mohanta S, Singh V, Yadav A, Kaushalendra K, Acharya A. Pharmacological Insights: Mitochondrial ROS Generation by FNC (Azvudine) in Dalton’s Lymphoma Cells Revealed by Super Resolution Imaging. Cell Biochemistry and Biophysics 2024;82(2):873 View
  55. Singh K, Kaur N, Prabhu A. Combating COVID-19 Crisis using Artificial Intelligence (AI) Based Approach: Systematic Review. Current Topics in Medicinal Chemistry 2024;24(8):737 View
  56. Escuissato D, Carvalho H. Perspectivas da Aplicação de Recursos de Inteligência Artificial na Covid-19. Computação Brasil 2020;(42):12 View
  57. Galetsi P, Katsaliaki K, Kumar S. Realizing Resilient Global Market Opportunities and Societal Benefits Through Innovative Digital Technologies in the Post COVID-19 Era: A Conceptual Framework and Critical Literature Review. IEEE Transactions on Engineering Management 2024;71:10650 View
  58. Shanbehzadeh M, Nopour R, Kazemi-Arpanahi H. Developing an artificial neural network for detecting COVID-19 disease. Journal of Education and Health Promotion 2022;11(1):2 View
  59. 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
  60. Kim T, Lee H. Evaluating AI Models and Predictors for COVID-19 Infection Dependent on Data from Patients with Cancer or Not: A Systematic Review. Korean Journal of Clinical Pharmacy 2024;34(3):141 View

Books/Policy Documents

  1. Chiari M, Gerevini A, Olivato M, Putelli L, Rossetti N, Serina I. Artificial Intelligence in Medicine. View
  2. Segall R. Biomedical and Business Applications Using Artificial Neural Networks and Machine Learning. View
  3. Khadela A, Popat S, Ajabiya J, Valu D, Savale S, Chavda V. Bioinformatics Tools for Pharmaceutical Drug Product Development. View
  4. Mena-Camilo E, Hernández-Nava G, Leyva-López S, Salazar-Colores S. XLVI Mexican Conference on Biomedical Engineering. View