Published on in Vol 23, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23458, first published .
Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study

Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study

Using Automated Machine Learning to Predict the Mortality of Patients With COVID-19: Prediction Model Development Study

Journals

  1. Karthikeyan A, Garg A, Vinod P, Priyakumar U. Machine Learning Based Clinical Decision Support System for Early COVID-19 Mortality Prediction. Frontiers in Public Health 2021;9 View
  2. Park M, Jo H, Lee H, Jung S, Hwang H. Machine Learning-Based COVID-19 Patients Triage Algorithm Using Patient-Generated Health Data from Nationwide Multicenter Database. Infectious Diseases and Therapy 2022;11(2):787 View
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  4. Chen L, Sheu J, Chuang Y, Tsao Y. Predicting the Travel Distance of Patients to Access Healthcare Using Deep Neural Networks. IEEE Journal of Translational Engineering in Health and Medicine 2022;10:1 View
  5. 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
  6. Nazir A, Ampadu H. Interpretable deep learning for the prediction of ICU admission likelihood and mortality of COVID-19 patients. PeerJ Computer Science 2022;8:e889 View
  7. Matysek A, Studnicka A, Smith W, Hutny M, Gajewski P, Filipiak K, Goh J, Yang G. Influence of Co-morbidities During SARS-CoV-2 Infection in an Indian Population. Frontiers in Medicine 2022;9 View
  8. Mustafa A. Mohammad R, Aljabri M, Aboulnour M, Mirza S, Alshobaiki A, Ramachandran M. Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques. Applied Computational Intelligence and Soft Computing 2022;2022:1 View
  9. Salcedo D, Guerrero C, Saeed K, Mardini J, Calderon-Benavides L, Henriquez C, Mendoza A. Machine Learning Algorithms Application in COVID-19 Disease: A Systematic Literature Review and Future Directions. Electronics 2022;11(23):4015 View
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  11. Ruan H, Tang Q, Zhang Y, Zhao X, Xiang Y, Feng Y, Cai W. Comparing human milk macronutrients measured using analyzers based on mid-infrared spectroscopy and ultrasound and the application of machine learning in data fitting. BMC Pregnancy and Childbirth 2022;22(1) View
  12. Lin F, Goebel B, Lee B, Lu Y, Baskaran L, Yoon Y, Maliakal G, Gianni U, Bax A, Sengupta P, Slomka P, Dey D, Rozanski A, Han D, Berman D, Budoff M, Miedema M, Nasir K, Rumberger J, Whelton S, Blaha M, Shaw L. Mortality impact of low CAC density predominantly occurs in early atherosclerosis: explainable ML in the CAC consortium. Journal of Cardiovascular Computed Tomography 2023;17(1):28 View
  13. Tang P, Zheng Y, Qiu W, Wang H, Guo J, Huang Z, Liu Z. Research on Anti-Alzheimer’s Traditional Chinese Medicine with Data Security: Datasets, Methods, and Evaluation. Security and Communication Networks 2022;2022:1 View
  14. 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
  15. Musigmann M, Akkurt B, Krähling H, Nacul N, Remonda L, Sartoretti T, Henssen D, Brokinkel B, Stummer W, Heindel W, Mannil M. Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology. Scientific Reports 2022;12(1) View
  16. Bai T, Zhu X, Zhou X, Grathwohl D, Yang P, Zha Y, Jin Y, Chong H, Yu Q, Isberner N, Wang D, Zhang L, Kortüm K, Song J, Rasche L, Einsele H, Ning K, Hou X. Reliable and Interpretable Mortality Prediction With Strong Foresight in COVID-19 Patients: An International Study From China and Germany. Frontiers in Artificial Intelligence 2021;4 View
  17. Ortiz-Barrios M, Arias-Fonseca S, Ishizaka A, Barbati M, Avendaño-Collante B, Navarro-Jiménez E. Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study. Journal of Business Research 2023;160:113806 View
  18. 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
  19. Peters G, Peelen R, Gilissen V, Koning M, van Harten W, Doggen C. Detecting Patient Deterioration Early Using Continuous Heart rate and Respiratory rate Measurements in Hospitalized COVID-19 Patients. Journal of Medical Systems 2023;47(1) View
  20. Jin S, Liu G, Bai Q. Deep Learning in COVID-19 Diagnosis, Prognosis and Treatment Selection. Mathematics 2023;11(6):1279 View
  21. Varelas G, Sakkopoulos E, Tzimas G, Phillips-Wren G, Mora M, Wang F, Gomez J. Exploring the role of country social and medical characteristics in patient level mortality in COVID-19 pandemic using Unsupervised Learning. Intelligent Decision Technologies 2022;16(1):231 View
  22. de Paiva B, Pereira P, de Andrade C, Gomes V, Souza-Silva M, Martins K, Sales T, de Carvalho R, Pires M, Ramos L, Silva R, de Freitas Martins Vieira A, Nunes A, de Oliveira Jorge A, de Oliveira Maurílio A, Scotton A, da Silva C, Cimini C, Ponce D, Pereira E, Manenti E, Rodrigues F, Anschau F, Botoni F, Bartolazzi F, Grizende G, Noal H, Duani H, Gomes I, Costa J, di Sabatino Santos Guimarães J, Tupinambás J, Rugolo J, Batista J, de Alvarenga J, Chatkin J, Ruschel K, Zandoná L, Pinheiro L, Menezes L, de Oliveira L, Kopittke L, Assis L, Marques L, Raposo M, Floriani M, Bicalho M, Nogueira M, de Oliveira N, Ziegelmann P, Paraiso P, de Lima Martelli P, Senger R, Menezes R, Francisco S, Araújo S, Kurtz T, Fereguetti T, de Oliveira T, Ribeiro Y, Ramires Y, Lima M, Carneiro M, Bezerra A, Schwarzbold A, de Moura Costa A, Farace B, Silveira D, de Almeida Cenci E, Lucas F, Aranha F, Bastos G, Vietta G, Nascimento G, Vianna H, Guimarães H, de Morais J, Moreira L, de Oliveira L, de Deus Sousa L, de Souza Viana L, de Souza Cabral M, Ferreira M, de Godoy M, de Figueiredo M, Guimarães-Junior M, de Paula de Sordi M, da Cunha Severino Sampaio N, Assaf P, Lutkmeier R, Valacio R, Finger R, de Freitas R, Guimarães S, Oliveira T, Diniz T, Gonçalves M, Marcolino M. Potential and limitations of machine meta-learning (ensemble) methods for predicting COVID-19 mortality in a large inhospital Brazilian dataset. Scientific Reports 2023;13(1) View
  23. Hu Y, Chen R, Gao H, Lin H, Wang J, Wang X, Liu J, Zeng Y. Explainable machine learning model for predicting spontaneous bacterial peritonitis in cirrhotic patients with ascites. Scientific Reports 2021;11(1) View
  24. Cisterna-García A, Guillén-Teruel A, Caracena M, Pérez E, Jiménez F, Francisco-Verdú F, Reina G, González-Billalabeitia E, Palma J, Sánchez-Ferrer Á, Botía J. A predictive model for hospitalization and survival to COVID-19 in a retrospective population-based study. Scientific Reports 2022;12(1) View
  25. Yang F, Qiao Y, Qi Y, Bo J, Wang X. BACS: blockchain and AutoML-based technology for efficient credit scoring classification. Annals of Operations Research 2022 View
  26. Syed A, Khan T, Alromema N. A Hybrid Feature Selection Approach to Screen a Novel Set of Blood Biomarkers for Early COVID-19 Mortality Prediction. Diagnostics 2022;12(7):1604 View
  27. Hanna M, Hanna M. Current applications and challenges of artificial intelligence in pathology. Human Pathology Reports 2022;27:300596 View
  28. Baker T, Loh W, Piasecki T, Bolt D, Smith S, Slutske W, Conner K, Bernstein S, Fiore M. A machine learning analysis of correlates of mortality among patients hospitalized with COVID-19. Scientific Reports 2023;13(1) View
  29. Yangchen T, Koraishy F, Xu C, Hou W, Rohatgi R, Wang Y. Initial mean arterial blood pressure (MABP) measurement is a risk factor for mortality in hypertensive COVID-19 positive hospitalized patients. PLOS ONE 2023;18(3):e0283331 View
  30. Bello B, Bundey Y, Bhave R, Khotimchenko M, Baran S, Chakravarty K, Varshney J. Integrating AI/ML Models for Patient Stratification Leveraging Omics Dataset and Clinical Biomarkers from COVID-19 Patients: A Promising Approach to Personalized Medicine. International Journal of Molecular Sciences 2023;24(7):6250 View
  31. Yazdani A, Bigdeli S, Zahmatkeshan M. Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran. Health Science Reports 2023;6(4) View
  32. Dobrijević D, Antić J, Rakić G, Katanić J, Andrijević L, Pastor K. Clinical Hematochemical Parameters in Differential Diagnosis between Pediatric SARS-CoV-2 and Influenza Virus Infection: An Automated Machine Learning Approach. Children 2023;10(5):761 View
  33. Kablan R, Miller H, Suliman S, Frieboes H. Evaluation of stacked ensemble model performance to predict clinical outcomes: A COVID-19 study. International Journal of Medical Informatics 2023;175:105090 View
  34. 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
  35. Musigmann M, Nacul N, Kasap D, Heindel W, Mannil M. Use Test of Automated Machine Learning in Cancer Diagnostics. Diagnostics 2023;13(14):2315 View
  36. Zhu H, Zhu Z, Wang S, Zhang Y. CovC-ReDRNet: A Deep Learning Model for COVID-19 Classification. Machine Learning and Knowledge Extraction 2023;5(3):684 View
  37. Rajkumar E, Nguyen K, Radic S, Paa J, Geng Q. Machine Learning and Causal Approaches to Predict Readmissions and Its Economic Consequences Among Canadian Patients With Heart Disease: Retrospective Study. JMIR Formative Research 2023;7:e41725 View
  38. 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
  39. 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
  40. Shayegan M. A brief review and scientometric analysis on ensemble learning methods for handling COVID-19. Heliyon 2024;10(4):e26694 View
  41. 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
  42. Tariq M, Ismail S, Xu C. Deep learning in public health: Comparative predictive models for COVID-19 case forecasting. PLOS ONE 2024;19(3):e0294289 View
  43. Thirunavukarasu A, Elangovan K, Gutierrez L, Hassan R, Li Y, Tan T, Cheng H, Teo Z, Lim G, Ting D. Clinical performance of automated machine learning: A systematic review. Annals of the Academy of Medicine, Singapore 2024;53(3):187 View
  44. Kim G, Ju C, Seok H, Lee D. Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients. Applied Sciences 2024;14(7):2715 View
  45. Thirunavukarasu A, Elangovan K, Gutierrez L, Hassan R, Li Y, Tan T, Cheng H, Teo Z, Lim G, Ting D. Clinical performance of automated machine learning: A systematic review. Annals of the Academy of Medicine, Singapore 2024;53(3 - Correct DOI):187 View
  46. Seyedtabib M, Najafi-Vosough R, Kamyari N. The predictive power of data: machine learning analysis for Covid-19 mortality based on personal, clinical, preclinical, and laboratory variables in a case–control study. BMC Infectious Diseases 2024;24(1) View

Books/Policy Documents

  1. Blagojević A, Geroski T. Applied Artificial Intelligence: Medicine, Biology, Chemistry, Financial, Games, Engineering. View
  2. Siam N, Khan M, Rownak M, Juel M, Uddin A. Machine Intelligence and Emerging Technologies. View
  3. Dhote S, Roberts M, Sridhar K. Innovations in VLSI, Signal Processing and Computational Technologies. View