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

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
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  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
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  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
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  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
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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