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

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24246, first published .
A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation

Journals

  1. Sang S, Sun R, Coquet J, Carmichael H, Seto T, Hernandez-Boussard T. Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study. Journal of Medical Internet Research 2021;23(2):e23026 View
  2. 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
  3. Subudhi S, Verma A, Patel A, Hardin C, Khandekar M, Lee H, McEvoy D, Stylianopoulos T, Munn L, Dutta S, Jain R. Comparing machine learning algorithms for predicting ICU admission and mortality in COVID-19. npj Digital Medicine 2021;4(1) View
  4. Cobre A, Stremel D, Noleto G, Fachi M, Surek M, Wiens A, Tonin F, Pontarolo R. Diagnosis and prediction of COVID-19 severity: can biochemical tests and machine learning be used as prognostic indicators?. Computers in Biology and Medicine 2021;134:104531 View
  5. Aljouie A, Almazroa A, Bokhari Y, Alawad M, Mahmoud E, Alawad E, Alsehawi A, Rashid M, Alomair L, Almozaai S, Albesher B, Alomaish H, Daghistani R, Alharbi N, Alaamery M, Bosaeed M, Alshaalan H. Early Prediction of COVID-19 Ventilation Requirement and Mortality from Routinely Collected Baseline Chest Radiographs, Laboratory, and Clinical Data with Machine Learning. Journal of Multidisciplinary Healthcare 2021;Volume 14:2017 View
  6. 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
  7. Hong W, Zhou X, Jin S, Lu Y, Pan J, Lin Q, Yang S, Xu T, Basharat Z, Zippi M, Fiorino S, Tsukanov V, Stock S, Grottesi A, Chen Q, Pan J. A Comparison of XGBoost, Random Forest, and Nomograph for the Prediction of Disease Severity in Patients With COVID-19 Pneumonia: Implications of Cytokine and Immune Cell Profile. Frontiers in Cellular and Infection Microbiology 2022;12 View
  8. Shanbehzadeh M, Yazdani A, Shafiee M, Kazemi-Arpanahi H. Predictive modeling for COVID-19 readmission risk using machine learning algorithms. BMC Medical Informatics and Decision Making 2022;22(1) View
  9. Kim J, Lim H, Ahn J, Lee K, Lee K, Koo K. Optimal Triage for COVID-19 Patients Under Limited Health Care Resources With a Parsimonious Machine Learning Prediction Model and Threshold Optimization Using Discrete-Event Simulation: Development Study. JMIR Medical Informatics 2021;9(11):e32726 View
  10. Ortíz-Barrios M, Coba-Blanco D, Alfaro-Saíz J, Stand-González D. Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review. International Journal of Environmental Research and Public Health 2021;18(16):8814 View
  11. Jung C, Mamandipoor B, Fjølner J, Bruno R, Wernly B, Artigas A, Bollen Pinto B, Schefold J, Wolff G, Kelm M, Beil M, Sviri S, van Heerden P, Szczeklik W, Czuczwar M, Elhadi M, Joannidis M, Oeyen S, Zafeiridis T, Marsh B, Andersen F, Moreno R, Cecconi M, Leaver S, De Lange D, Guidet B, Flaatten H, Osmani V. Disease-Course Adapting Machine Learning Prognostication Models in Elderly Patients Critically Ill With COVID-19: Multicenter Cohort Study With External Validation. JMIR Medical Informatics 2022;10(3):e32949 View
  12. Zhang K, Karanth S, Patel B, Murphy R, Jiang X. A multi-task Gaussian process self-attention neural network for real-time prediction of the need for mechanical ventilators in COVID-19 patients. Journal of Biomedical Informatics 2022;130:104079 View
  13. He F, Page J, Weinberg K, Mishra A. The Development and Validation of Simplified Machine Learning Algorithms to Predict Prognosis of Hospitalized Patients With COVID-19: Multicenter, Retrospective Study. Journal of Medical Internet Research 2022;24(1):e31549 View
  14. Rai N, Kaushik N, Kumar D, Raj C, Ali A. Mortality prediction of COVID-19 patients using soft voting classifier. International Journal of Cognitive Computing in Engineering 2022;3:172 View
  15. Avelino-Silva V, Avelino-Silva T, Aliberti M, Ferreira J, Cobello Junior V, Silva K, Pompeu J, Antonangelo L, Magri M, Filho T, Souza H, Kallás E. Prediction of intensive care admission and hospital mortality in COVID-19 patients using demographics and baseline laboratory data. Clinics 2023;78:100183 View
  16. 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
  17. Bendavid I, Statlender L, Shvartser L, Teppler S, Azullay R, Sapir R, Singer P. A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19. Scientific Reports 2022;12(1) View
  18. Ku Y, Kwon S, Yoon J, Mun S, Chang M. Machine Learning Models for Predicting the Occurrence of Respiratory Diseases Using Climatic and Air-Pollution Factors. Clinical and Experimental Otorhinolaryngology 2022;15(2):168 View
  19. 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
  20. 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
  21. 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
  22. Isaev E, Ermanova M, Sidle R, Zaginaev V, Kulikov M, Chontoev D. Reconstruction of Hydrometeorological Data Using Dendrochronology and Machine Learning Approaches to Bias-Correct Climate Models in Northern Tien Shan, Kyrgyzstan. Water 2022;14(15):2297 View
  23. Varzaneh Z, Orooji A, Erfannia L, Shanbehzadeh M. A new COVID-19 intubation prediction strategy using an intelligent feature selection and K-NN method. Informatics in Medicine Unlocked 2022;28:100825 View
  24. Okuyucu M, Tunç T, Güllü Y, Bozkurt İ, Esen M, Öztürk O. A novel intubation prediction model for patients hospitalized with COVID-19: the OTO-COVID-19 scoring model. Current Medical Research and Opinion 2022;38(9):1509 View
  25. Abdeltawab H, Khalifa F, ElNakieb Y, Elnakib A, Taher F, Alghamdi N, Sandhu H, El-Baz A. Predicting the Level of Respiratory Support in COVID-19 Patients Using Machine Learning. Bioengineering 2022;9(10):536 View
  26. Karlafti E, Anagnostis A, Kotzakioulafi E, Vittoraki M, Eufraimidou A, Kasarjyan K, Eufraimidou K, Dimitriadou G, Kakanis C, Anthopoulos M, Kaiafa G, Savopoulos C, Didangelos T. Does COVID-19 Clinical Status Associate with Outcome Severity? An Unsupervised Machine Learning Approach for Knowledge Extraction. Journal of Personalized Medicine 2021;11(12):1380 View
  27. 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
  28. Zhou Y, Ge Y, Yang X, Cai Q, Ding Y, Hu L, Lu G. Prevalence and Outcomes of Pancreatic Enzymes Elevation in Patients With COVID-19: A Meta-Analysis and Systematic Review. Frontiers in Public Health 2022;10 View
  29. Chang W, Ji X, Wang L, Liu H, Zhang Y, Chen B, Zhou S. A Machine-Learning Method of Predicting Vital Capacity Plateau Value for Ventilatory Pump Failure Based on Data Mining. Healthcare 2021;9(10):1306 View
  30. Winston L, McCann M, Onofrei G. Exploring Socioeconomic Status as a Global Determinant of COVID-19 Prevalence, Using Exploratory Data Analytic and Supervised Machine Learning Techniques: Algorithm Development and Validation Study. JMIR Formative Research 2022;6(9):e35114 View
  31. Zhang X, Fei N, Zhang X, Wang Q, Fang Z. Machine Learning Prediction Models for Postoperative Stroke in Elderly Patients: Analyses of the MIMIC Database. Frontiers in Aging Neuroscience 2022;14 View
  32. Campbell T, Wilson M, Roder H, MaWhinney S, Georgantas R, Maguire L, Roder J, Erlandson K. Predicting prognosis in COVID-19 patients using machine learning and readily available clinical data. International Journal of Medical Informatics 2021;155:104594 View
  33. Mertz L. AI Tools Poised to Improve Patient Health Care. IEEE Pulse 2022;13(2):2 View
  34. Föll S, Lison A, Maritsch M, Klingberg K, Lehmann V, Züger T, Srivastava D, Jegerlehner S, Feuerriegel S, Fleisch E, Exadaktylos A, Wortmann F. A Scalable Risk-Scoring System Based on Consumer-Grade Wearables for Inpatients With COVID-19: Statistical Analysis and Model Development. JMIR Formative Research 2022;6(6):e35717 View
  35. Tredinnick-Rowe J, Symonds R. Rapid systematic review of respiratory rate as a vital sign of clinical deterioration in COVID-19. Expert Review of Respiratory Medicine 2022;16(11-12):1227 View
  36. van Goor H, Vernooij L, Breteler M, Kalkman C, Kaasjager K, van Loon K. Association of Continuously Measured Vital Signs With Respiratory Insufficiency in Hospitalized COVID-19 Patients: Retrospective Cohort Study. Interactive Journal of Medical Research 2022;11(2):e40289 View
  37. Hosseinzadeh Kasani P, Lee J, Park C, Yun C, Jang J, Lee S. Evaluation of nutritional status and clinical depression classification using an explainable machine learning method. Frontiers in Nutrition 2023;10 View
  38. Debnath S, Koppel R, Saadi N, Potak D, Weinberger B, Zanos T. Prediction of intrapartum fever using continuously monitored vital signs and heart rate variability. DIGITAL HEALTH 2023;9 View
  39. Aqel S, Syaj S, Al-Bzour A, Abuzanouneh F, Al-Bzour N, Ahmad J. Artificial Intelligence and Machine Learning Applications in Sudden Cardiac Arrest Prediction and Management: A Comprehensive Review. Current Cardiology Reports 2023;25(11):1391 View
  40. Mehrdad S, Shamout F, Wang Y, Atashzar S. Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs. Scientific Reports 2023;13(1) View
  41. 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
  42. Lashen H, St John T, Almallah Y, Sasidhar M, Shamout F. Machine Learning Models Versus the National Early Warning Score System for Predicting Deterioration: Retrospective Cohort Study in the United Arab Emirates. JMIR AI 2023;2:e45257 View
  43. Zhong X, Lin Y, Zhang W, Bi Q. Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning. Scientific Reports 2023;13(1) View
  44. Souza de Abreu R, Silva I, Nunes Y, Moioli R, Guedes L. Advancing Fault Prediction: A Comparative Study between LSTM and Spiking Neural Networks. Processes 2023;11(9):2772 View
  45. Wei S, Zhang Y, Dong H, Chen Y, Wang X, Zhu X, Zhang G, Guo S. Machine learning-based prediction model of acute kidney injury in patients with acute respiratory distress syndrome. BMC Pulmonary Medicine 2023;23(1) View
  46. Luckscheiter A, Zink W, Lohs T, Eisenberger J, Thiel M, Viergutz T. Machine learning for the prediction of preclinical airway management in injured patients: a registry-based trial. Clinical and Experimental Emergency Medicine 2022;9(4):304 View
  47. Zhang P, Wu L, Zou T, Zou Z, Tu J, Gong R, Kuang J. Machine Learning for Early Prediction of Major Adverse Cardiovascular Events After First Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Retrospective Cohort Study. JMIR Formative Research 2024;8:e48487 View
  48. Im J, Yoon S, Shin Y, Park S. Real-Time Prediction for Neonatal Endotracheal Intubation Using Multimodal Transformer Network. IEEE Journal of Biomedical and Health Informatics 2023;27(6):2625 View
  49. Cho K, Kim E, Kim J, Yun C, Jang J, Kasani P, Jo H. Comparative effectiveness of explainable machine learning approaches for extrauterine growth restriction classification in preterm infants using longitudinal data. Frontiers in Medicine 2023;10 View
  50. 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
  51. Didier A, Nigro A, Noori Z, Omballi M, Pappada S, Hamouda D. Application of machine learning for lung cancer survival prognostication—A systematic review and meta-analysis. Frontiers in Artificial Intelligence 2024;7 View
  52. Ji B, Kong L, Wang J, Liu C, Yuan K, Zhu L, Liang H. Predicting the prognosis of patients with mild COVID-19 by chest CT based on machine learning. Chinese Journal of Academic Radiology 2024;7(2):157 View
  53. Ding H, Feng X, Yang Q, Yang Y, Zhu S, Ji X, Kang Y, Shen J, Zhao M, Xu S, Ning G, Xu Y. A risk prediction model for efficient intubation in the emergency department: A 4‐year single‐center retrospective analysis. Journal of the American College of Emergency Physicians Open 2024;5(3) View

Books/Policy Documents

  1. Danker C, Murzabekov M, Forsberg D, Lidströmer N, Honoré A, Rautiainen S, Herlenius E. Artificial Intelligence in Covid-19. View
  2. Vavougios G, Zarogiannis S, Gourgoulianis K. Omics approaches and technologies in COVID-19. View
  3. Garg A, Venkataramani V, Karthikeyan A, Priyakumar U. Distributed Computing and Intelligent Technology. View