Published on in Vol 22, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24225, first published .
An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study

An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study

An Easy-to-Use Machine Learning Model to Predict the Prognosis of Patients With COVID-19: Retrospective Cohort Study

Journals

  1. Pelletier J, Rakkar J, Au A, Fuhrman D, Clark R, Horvat C. Trends in US Pediatric Hospital Admissions in 2020 Compared With the Decade Before the COVID-19 Pandemic. JAMA Network Open 2021;4(2):e2037227 View
  2. Oh B, Hwangbo S, Jung T, Min K, Lee C, Apio C, Lee H, Lee S, Moon M, Kim S, Park T. Prediction Models for the Clinical Severity of Patients With COVID-19 in Korea: Retrospective Multicenter Cohort Study. Journal of Medical Internet Research 2021;23(4):e25852 View
  3. 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
  4. 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
  5. Heo J, Yoo J, Lee H, Lee I, Kim J, Park E, Kim Y, Nam H. Prediction of Hidden Coronary Artery Disease Using Machine Learning in Patients With Acute Ischemic Stroke. Neurology 2022;99(1) View
  6. Hwangbo S, Kim Y, Lee C, Lee S, Oh B, Moon M, Kim S, Park T. Machine learning models to predict the maximum severity of COVID-19 based on initial hospitalization record. Frontiers in Public Health 2022;10 View
  7. Katoto P, Aboubacar I, Oumarou B, Adehossi E, Anya B, Mounkaila A, Moustapha A, Ishagh E, Diawara G, Nsiari-Muzeyi B, Didier T, Wiysonge C. Clinical features and predictors of mortality among hospitalized patients with COVID-19 in Niger. Conflict and Health 2021;15(1) View
  8. Chiu H, Hwang C, Chen S, Shih F, Han H, King C, Gilbert J, Fang C, Oyang Y. Machine learning for emerging infectious disease field responses. Scientific Reports 2022;12(1) View
  9. Zhao Y, Chen Q, Liu T, Luo P, Zhou Y, Liu M, Xiong B, Zhou F. Development and Validation of Predictors for the Survival of Patients With COVID-19 Based on Machine Learning. Frontiers in Medicine 2021;8 View
  10. Corica B, Tartaglia F, D’Amico T, Romiti G, Cangemi R. Sex and gender differences in community-acquired pneumonia. Internal and Emergency Medicine 2022;17(6):1575 View
  11. Koshechkin K, Lebedev G, Fartushnyi E, Orlov Y. Holistic Approach for Artificial Intelligence Implementation in Pharmaceutical Products Lifecycle: A Meta-Analysis. Applied Sciences 2022;12(16):8373 View
  12. Wani S, Khan N, Thakur G, Gautam S, Ali M, Alam P, Alshehri S, Ghoneim M, Shakeel F. Utilization of Artificial Intelligence in Disease Prevention: Diagnosis, Treatment, and Implications for the Healthcare Workforce. Healthcare 2022;10(4):608 View
  13. Kim H, Heo J, Han D, Oh H. Validation of Machine Learning Models to Predict Adverse Outcomes in Patients with COVID-19: A Prospective Pilot Study. Yonsei Medical Journal 2022;63(5):422 View
  14. 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
  15. Saadatmand S, Salimifard K, Mohammadi R, Kuiper A, Marzban M, Farhadi A. Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients. Annals of Operations Research 2023;328(1):1043 View
  16. 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
  17. Baik S, Lee M, Hong K, Park D. Development of Machine-Learning Model to Predict COVID-19 Mortality: Application of Ensemble Model and Regarding Feature Impacts. Diagnostics 2022;12(6):1464 View
  18. 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
  19. 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
  20. Michelucci U, Sperti M, Piga D, Venturini F, Deriu M. A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification. Algorithms 2021;14(11):301 View
  21. Afiahayati , Wah Y, Hartati S, Sari Y, Trisna I, Putri D, Musdholifah A, Wardoyo R. Forecasting the Cumulative COVID-19 Cases in Indonesia Using Flower Pollination Algorithm. Computation 2022;10(12):214 View
  22. Araújo D, Veloso A, Borges K, Carvalho M. Prognosing the risk of COVID-19 death through a machine learning-based routine blood panel: A retrospective study in Brazil. International Journal of Medical Informatics 2022;165:104835 View
  23. Bhat S, Pandey A, Kanakan A, Maurya R, Vasudevan J, Devi P, Chattopadhyay P, Sharma S, Khyalappa R, Joshi M, Pandey R. Learning From Biological and Computational Machines: Importance of SARS-CoV-2 Genomic Surveillance, Mutations and Risk Stratification. Frontiers in Cellular and Infection Microbiology 2021;11 View
  24. 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
  25. 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
  26. Karpov O, Pitsik E, Kurkin S, Maksimenko V, Gusev A, Shusharina N, Hramov A. Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach. International Journal of Environmental Research and Public Health 2023;20(7):5335 View
  27. Wang J, Chen H, Wang H, Liu W, Peng D, Zhao Q, Xiao M. A Risk Prediction Model for Physical Restraints Among Older Chinese Adults in Long-term Care Facilities: Machine Learning Study. Journal of Medical Internet Research 2023;25:e43815 View
  28. Natanov D, Avihai B, McDonnell E, Lee E, Cook B, Altomare N, Ko T, Chaia A, Munoz C, Ouellette S, Nyalakonda S, Cederbaum V, Parikh P, Blaser M, Moscona A. Predicting COVID-19 prognosis in hospitalized patients based on early status. mBio 2023;14(5) View
  29. Zheng J, Li J, Zhang Z, Yu Y, Tan J, Liu Y, Gong J, Wang T, Wu X, Guo Z. Clinical Data based XGBoost Algorithm for infection risk prediction of patients with decompensated cirrhosis: a 10-year (2012–2021) Multicenter Retrospective Case-control study. BMC Gastroenterology 2023;23(1) View
  30. Mohammedain S, Badran S, Elzouki A, Salim H, Chalaby A, Siddiqui M, Hussein Y, Rahim H, Thalib L, Alam M, Al-Badriyeh D, Al-Maadeed S, Doi S. Validation of a Risk Prediction Model for COVID-19: The PERIL Prospective Cohort Study. Future Virology 2023;18(15):991 View
  31. SenthilKumar G, Madhusudhana S, Flitcroft M, Sheriff S, Thalji S, Merrill J, Clarke C, Maduekwe U, Tsai S, Christians K, Gamblin T, Kothari A. Automated machine learning (AutoML) can predict 90-day mortality after gastrectomy for cancer. Scientific Reports 2023;13(1) View
  32. de Holanda W, e Silva L, de Carvalho César Sobrinho Á. Machine learning models for predicting hospitalization and mortality risks of COVID-19 patients. Expert Systems with Applications 2024;240:122670 View
  33. Viderman D, Kotov A, Popov M, Abdildin Y. Machine and deep learning methods for clinical outcome prediction based on physiological data of COVID-19 patients: a scoping review. International Journal of Medical Informatics 2024;182:105308 View
  34. 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
  35. Schaffert D, Bibi I, Blauth M, Lull C, von Ahnen J, Gross G, Schulze-Hagen T, Knitza J, Kuhn S, Benecke J, Schmieder A, Leipe J, Olsavszky V. Using Automated Machine Learning to Predict Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris and Psoriatic Arthritis and Uncover New Influences on Disease Progression: Retrospective Study. JMIR Formative Research 2024;8:e55855 View
  36. Zhang Y, Li Q, Duan H, Tan L, Cao Y, Chen J. Machine learning based predictive modeling and risk factors for prolonged SARS-CoV-2 shedding. Journal of Translational Medicine 2024;22(1) View
  37. Ceylan B, Olmuşçelik O, Karaalioğlu B, Ceylan Ş, Şahin M, Aydın S, Yılmaz E, Dumlu R, Kapmaz M, Çiçek Y, Kansu A, Duger M, Mert A. Predicting Severe Respiratory Failure in Patients with COVID-19: A Machine Learning Approach. Journal of Clinical Medicine 2024;13(23):7386 View

Books/Policy Documents

  1. Sharma R, Pandey H, Agarwal A, Srivastava D. Proceedings of Fourth Doctoral Symposium on Computational Intelligence. View