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

Theme Issue: Medical Informatics and COVID-19 (107) Artificial Intelligence (1421) JMIR Theme Issue: COVID-19 Special Issue (2461) Computer-Aided Diagnosis (135) Clinical Information and Decision Making (1434) Outbreak and Pandemic Preparedness and Management (1743) Decision Support for Health Professionals (1210) Infectious Diseases (non-STD/STI) (1592) Machine Learning (1501)

Published on in Vol 9, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25884, first published .
Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development

Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development

Machine Learning Approach to Predicting COVID-19 Disease Severity Based on Clinical Blood Test Data: Statistical Analysis and Model Development

Authors of this article:

Sakifa Aktar1 Author Orcid Image ;   Md Martuza Ahamad1 Author Orcid Image ;   Md Rashed-Al-Mahfuz2 Author Orcid Image ;   AKM Azad3 Author Orcid Image ;   Shahadat Uddin4 Author Orcid Image ;   AHM Kamal5 Author Orcid Image ;   Salem A Alyami6 Author Orcid Image ;   Ping-I Lin7 Author Orcid Image ;   Sheikh Mohammed Shariful Islam8 Author Orcid Image ;   Julian MW Quinn9 Author Orcid Image ;   Valsamma Eapen7 Author Orcid Image ;   Mohammad Ali Moni7, 9, 10 Author Orcid Image
Article Authors Cited by (59) Tweetations (6) Metrics

Journals

  1. Satu M, Howlader K, Mahmud M, Kaiser M, Shariful Islam S, Quinn J, Alyami S, Moni M. Short-Term Prediction of COVID-19 Cases Using Machine Learning Models. Applied Sciences 2021;11(9):4266 View
  2. Aktar S, Talukder A, Ahamad M, Kamal A, Khan J, Protikuzzaman M, Hossain N, Azad A, Quinn J, Summers M, Liaw T, Eapen V, Moni M. Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19. Diagnostics 2021;11(8):1383 View
  3. Yu Z, He L, Luo W, Tse R, Pau G. Deep Learning Hybrid Models for COVID-19 Prediction. Journal of Global Information Management 2022;30(10):1 View
  4. 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
  5. Ahamed K, Islam M, Uddin A, Akhter A, Paul B, Yousuf M, Uddin S, Quinn J, Moni M. A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images. Computers in Biology and Medicine 2021;139:105014 View
  6. 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
  7. Abdalrada A, Abawajy J, Al-Quraishi T, Islam S. Machine learning models for prediction of co-occurrence of diabetes and cardiovascular diseases: a retrospective cohort study. Journal of Diabetes & Metabolic Disorders 2022;21(1):251 View
  8. Ma R, Zheng X, Wang P, Liu H, Zhang C. The prediction and analysis of COVID-19 epidemic trend by combining LSTM and Markov method. Scientific Reports 2021;11(1) View
  9. Christakis N, Tirchas P, Politis M, Achladianakis M, Avgenikou E, Kossioris G. COVID-LIBERTY, A Machine Learning Computational Framework for the Study of the Covid-19 Pandemic in Europe. Part 2: Setting up the Framework with Ensemble Modeling. International Journal of Neural Networks and Advanced Applications 2021;8:27 View
  10. Hatmal M, Al-Hatamleh M, Olaimat A, Mohamud R, Fawaz M, Kateeb E, Alkhairy O, Tayyem R, Lounis M, Al-Raeei M, Dana R, Al-Ameer H, Taha M, Bindayna K. Reported Adverse Effects and Attitudes among Arab Populations Following COVID-19 Vaccination: A Large-Scale Multinational Study Implementing Machine Learning Tools in Predicting Post-Vaccination Adverse Effects Based on Predisposing Factors. Vaccines 2022;10(3):366 View
  11. Dairi A, Harrou F, Sun Y. Deep Generative Learning-Based 1-SVM Detectors for Unsupervised COVID-19 Infection Detection Using Blood Tests. IEEE Transactions on Instrumentation and Measurement 2022;71:1 View
  12. Doyle R. Machine Learning–Based Prediction of COVID-19 Mortality With Limited Attributes to Expedite Patient Prognosis and Triage: Retrospective Observational Study. JMIRx Med 2021;2(4):e29392 View
  13. Liu X, Hasan M, Ahmed K, Hossain M. Machine learning to analyse omic-data for COVID-19 diagnosis and prognosis. BMC Bioinformatics 2023;24(1) View
  14. Alabbad D, Almuhaideb A, Alsunaidi S, Alqudaihi K, Alamoudi F, Alhobaishi M, Alaqeel N, Alshahrani M. Machine learning model for predicting the length of stay in the intensive care unit for Covid-19 patients in the eastern province of Saudi Arabia. Informatics in Medicine Unlocked 2022;30:100937 View
  15. Effah C, Miao R, Drokow E, Agboyibor C, Qiao R, Wu Y, Miao L, Wang Y. Machine learning-assisted prediction of pneumonia based on non-invasive measures. Frontiers in Public Health 2022;10 View
  16. Chowdhury U, Faruqe M, Mehedy M, Ahmad S, Islam M, Shoombuatong W, Azad A, Moni M. Effects of Bacille Calmette Guerin (BCG) vaccination during COVID-19 infection. Computers in Biology and Medicine 2021;138:104891 View
  17. 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
  18. Phuong J, Hyland S, Mooney S, Long D, Takeda K, Vavilala M, O’Hara K, Khubchandani J. Sociodemographic and clinical features predictive of SARS-CoV-2 test positivity across healthcare visit-types. PLOS ONE 2021;16(10):e0258339 View
  19. Saada H, Pagneux Q, Wei J, Live L, Roussel A, Dogliani A, Die Morini L, Engelmann I, Alidjinou E, Rolland A, Faure E, Poissy J, Labreuche J, Lee G, Li P, Curran G, Jawhari A, Yunda J, Melinte S, Legay A, Gala J, Devos D, Boukherroub R, Szunerits S. Sensing of COVID-19 spike protein in nasopharyngeal samples using a portable surface plasmon resonance diagnostic system. Sensors & Diagnostics 2022;1(5):1021 View
  20. Abayomi-Alli O, Damaševičius R, Maskeliūnas R, Misra S. An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples. Sensors 2022;22(6):2224 View
  21. 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
  22. Kwasniewski M, Korotko U, Chwialkowska K, Niemira M, Jaroszewicz J, Sobala-Szczygiel B, Puzanowska B, Moniuszko-Malinowska A, Pancewicz S, Parfieniuk-Kowerda A, Martonik D, Zarębska-Michaluk D, Simon K, Pazgan-Simon M, Mozer-Lisewska I, Bura M, Adamek A, Tomasiewicz K, Pawłowska M, Piekarska A, Berkan-Kawińska A, Horban A, Kowalska J, Podlasin R, Wasilewski P, Azzadin A, Czuczwar M, Borys M, Piwowarczyk P, Czaban S, Bogocz J, Ochab M, Kruk A, Uszok S, Bielska A, Szalkowska A, Raczkowska J, Sokolowska G, Chorostowska-Wynimko J, Jezela-Stanek A, Rozy A, Lechowicz U, Polowianiuk U, Tycinska A, Grubczak K, Starosz A, Izdebska W, Krzeminski T, Bousquet J, Sokolowska M, Franchini G, Hadlock J, Kretowski A, Eljaszewicz A, Flisiak R, Moniuszko M. Implementation of the User-Friendly Odds Ratio Calculator for Unvaccinated Individuals in a Country with a High COVID-19 Death Toll. SSRN Electronic Journal 2022 View
  23. Harrou F, Dairi A, Dorbane A, Kadri F, Sun Y. Semi-Supervised KPCA-Based Monitoring Techniques for Detecting COVID-19 Infection through Blood Tests. Diagnostics 2023;13(8):1466 View
  24. Rahman T, Chowdhury M, Khandakar A, Mahbub Z, Hossain M, Alhatou A, Abdalla E, Muthiyal S, Islam K, Kashem S, Khan M, Zughaier S, Hossain M. BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data. Neural Computing and Applications 2023;35(24):17461 View
  25. Khanna V, Chadaga K, Sampathila N, Prabhu S, P. R. A machine learning and explainable artificial intelligence triage-prediction system for COVID-19. Decision Analytics Journal 2023;7:100246 View
  26. Kessler R, Philipp J, Wilfer J, Kostev K. Predictive Attributes for Developing Long COVID—A Study Using Machine Learning and Real-World Data from Primary Care Physicians in Germany. Journal of Clinical Medicine 2023;12(10):3511 View
  27. Abbasi Habashi S, Koyuncu M, Alizadehsani R. A Survey of COVID-19 Diagnosis Using Routine Blood Tests with the Aid of Artificial Intelligence Techniques. Diagnostics 2023;13(10):1749 View
  28. Chadaga K, Prabhu S, Sampathila N, Chadaga R. Severity prediction in COVID-19 patients using clinical markers and explainable artificial intelligence: A stacked ensemble machine learning approach. Intelligent Decision Technologies 2023;17(4):959 View
  29. Pisano F, Cannas B, Fanni A, Pasella M, Canetto B, Giglio S, Mocci S, Chessa L, Perra A, Littera R. Decision trees for early prediction of inadequate immune response to coronavirus infections: a pilot study on COVID-19. Frontiers in Medicine 2023;10 View
  30. Al Shaqsi J, Borghan M, Drogham O, Al Whahaibi S. A machine learning approach to predict the parameters of COVID‐19 severity to improve the diagnosis protocol in Oman. SN Applied Sciences 2023;5(10) View
  31. Al Shaqsi J, Drogham O, Aburass S. Advanced machine learning based exploration for predicting pandemic fatality: Oman dataset. Informatics in Medicine Unlocked 2023;43:101393 View
  32. Uddin M, Ahamad M, Hoque M, Walid M, Aktar S, Alotaibi N, Alyami S, Kabir M, Moni M. A Comparison of Machine Learning Techniques for the Detection of Type-2 Diabetes Mellitus: Experiences from Bangladesh. Information 2023;14(7):376 View
  33. Farahat I, Aladrousy W, Elhoseny M, Tolba A, Elmougy S. CAD system for intelligent grading of COVID-19 severity with green computing and low carbon footprint analysis. Expert Systems with Applications 2023;234:121108 View
  34. Ghaderzadeh M, Asadi F, Ramezan Ghorbani N, Almasi S, Taami T. Toward artificial intelligence (AI) applications in the determination of COVID-19 infection severity: considering AI as a disease control strategy in future pandemics. Iranian Journal of Blood and Cancer 2023;15(3):93 View
  35. Tehrani S, Zarvani M, Amiri P, Ghods Z, Raoufi M, Safavi-Naini S, Soheili A, Gharib M, Abbasi H. Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data. BMC Medical Informatics and Decision Making 2023;23(1) View
  36. Hussain Z, Borah M, Ahmed R. Computational methods for studying relationship between nutritional status and respiratory viral diseases: a systematic review. Artificial Intelligence Review 2024;57(1) View
  37. Farahat I, Sharafeldeen A, Ghazal M, Alghamdi N, Mahmoud A, Connelly J, van Bogaert E, Zia H, Tahtouh T, Aladrousy W, Tolba A, Elmougy S, El-Baz A. An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis. Scientific Reports 2024;14(1) View
  38. Tutsoy O, Koç G. Deep self-supervised machine learning algorithms with a novel feature elimination and selection approaches for blood test-based multi-dimensional health risks classification. BMC Bioinformatics 2024;25(1) View
  39. Sagar D, Dwivedi T, Gupta A, Aggarwal P, Bhatnagar S, Mohan A, Kaur P, Gupta R. Clinical Features Predicting COVID-19 Severity Risk at the Time of Hospitalization. Cureus 2024 View
  40. 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
  41. Debnath A, Tarafdar A, Reddy A, Bhattacharya P. ROVM integrated advanced machine learning-based malaria prediction strategy in Tripura. The Journal of Supercomputing 2024;80(11):15725 View
  42. Shaheen R, Akram B, Zafar A, Waheed T. Comparative Analysis of Supervised Machine Learning Algorithms for COVID-19 Prediction. Sir Syed University Research Journal of Engineering & Technology 2024;14(1):12 View
  43. Ortiz-Barrios M, Ishizaka A, Barbati M, Arias-Fonseca S, Khan J, Gul M, Yücesan M, Alfaro-Saíz J, Pérez-Aguilar A. Integrating discrete-event simulation and artificial intelligence for shortening bed waiting times in hospitalization departments during respiratory disease seasons. Computers & Industrial Engineering 2024;194:110405 View
  44. Araujo Gomes G, Beltrão F, Fragoso W, Lemos S. Discrimination between Covid-19 positive and negative blood serum based on excitation-emission matrix fluorescence spectroscopy and chemometrics. Talanta 2024;280:126788 View
  45. Rayan R, Suruliandi A, Raja S. Modified mutual information feature selection algorithm to predict COVID-19 using clinical data. Computer Methods in Biomechanics and Biomedical Engineering 2024:1 View

Books/Policy Documents

  1. Ahmed S, Islam S. The Science behind the COVID Pandemic and Healthcare Technology Solutions. View
  2. Alhawas N, Kartal S. Science and Technologies for Smart Cities. View
  3. Guest P, Abbasifard M, Jamialahmadi T, Majeed M, Kesharwani P, Sahebkar A. Multiplex Biomarker Techniques. View
  4. Qi X, Shen L, Chen J, Shi M, Shen B. Translational Informatics. View
  5. Adibi S, Rajabifard A, Islam S, Ahmadvand A. The Science behind the COVID Pandemic and Healthcare Technology Solutions. View
  6. Ikramov A, Anvarov K, Sharipova V, Iskhakov N, Abdurakhmonov A, Alimov A. AI 2021: Advances in Artificial Intelligence. View
  7. Ani R, Deepa O, Arundhathi M, Darsana J. Innovations in Computer Science and Engineering. View
  8. Devkar A, Kanade A. AI Technologies for Information Systems and Management Science. View