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

JMIR Theme Issue: COVID-19 Special Issue (2458) Artificial Intelligence (1407) Clinical Information and Decision Making (1423) Decision Support for Health Professionals (1201) Machine Learning (1488) Infectious Diseases (non-STD/STI) (1585) Outbreak and Pandemic Preparedness and Management (1739) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1520)

Published on in Vol 22, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24048, first published .
Development and External Validation of a Machine Learning Tool to Rule Out COVID-19 Among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study

Development and External Validation of a Machine Learning Tool to Rule Out COVID-19 Among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study

Development and External Validation of a Machine Learning Tool to Rule Out COVID-19 Among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study

Authors of this article:

Timothy B Plante1, 2 Author Orcid Image ;   Aaron M Blau2 Author Orcid Image ;   Adrian N Berg3, 4 Author Orcid Image ;   Aaron S Weinberg5 Author Orcid Image ;   Ik C Jun5 Author Orcid Image ;   Victor F Tapson5 Author Orcid Image ;   Tanya S Kanigan4 Author Orcid Image ;   Artur B Adib4 Author Orcid Image
Article Authors Cited by (32) Tweetations (14) Metrics

Journals

  1. Gangloff C, Rafi S, Bouzillé G, Soulat L, Cuggia M. Machine learning is the key to diagnose COVID-19: a proof-of-concept study. Scientific Reports 2021;11(1) View
  2. Gladding P, Ayar Z, Smith K, Patel P, Pearce J, Puwakdandawa S, Tarrant D, Atkinson J, McChlery E, Hanna M, Gow N, Bhally H, Read K, Jayathissa P, Wallace J, Norton S, Kasabov N, Calude C, Steel D, Mckenzie C. A Machine Learning PROGRAM to Identify COVID-19 and Other Diseases From Hematology Data. Future Science OA 2021;7(7) View
  3. Cabitza F, Campagner A, Soares F, García de Guadiana-Romualdo L, Challa F, Sulejmani A, Seghezzi M, Carobene A. The importance of being external. methodological insights for the external validation of machine learning models in medicine. Computer Methods and Programs in Biomedicine 2021;208:106288 View
  4. Roland T, Böck C, Tschoellitsch T, Maletzky A, Hochreiter S, Meier J, Klambauer G. Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. Journal of Medical Systems 2022;46(5) View
  5. Dardenne N, Locquet M, Diep A, Gilbert A, Delrez S, Beaudart C, Brabant C, Ghuysen A, Donneau A, Bruyère O. Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study. BMC Infectious Diseases 2022;22(1) View
  6. Amin M, Wozniak M, Barbaric L, Pickard S, Yerrabelli R, Christensen A, Coiado O. Experimental Technologies in the Diagnosis and Treatment of COVID-19 in Patients with Comorbidities. Journal of Healthcare Informatics Research 2022;6(1):48 View
  7. Çubukçu H, Topcu D, Bayraktar N, Gülşen M, Sarı N, Arslan A. Detection of COVID-19 by Machine Learning Using Routine Laboratory Tests. American Journal of Clinical Pathology 2022;157(5):758 View
  8. Boer A, Deneer R, Maas M, Ammerlaan H, van Balkom R, Thijssen W, Bennenbroek S, Leers M, Martens R, Buijs M, Kerremans J, Messchaert M, van Suijlen J, van Riel N, Scharnhorst V. Development and validation of an early warning score to identify COVID-19 in the emergency department based on routine laboratory tests: a multicentre case–control study. BMJ Open 2022;12(8):e059111 View
  9. McRae A, Hohl C, Rosychuk R, Vatanpour S, Ghaderi G, Archambault P, Brooks S, Cheng I, Davis P, Hayward J, Lang E, Ohle R, Rowe B, Welsford M, Yadav K, Morrison L, Perry J. CCEDRRN COVID-19 Infection Score (CCIS): development and validation in a Canadian cohort of a clinical risk score to predict SARS-CoV-2 infection in patients presenting to the emergency department with suspected COVID-19. BMJ Open 2021;11(12):e055832 View
  10. Monday H, Li J, Nneji G, Nahar S, Hossin M, Jackson J, Ejiyi C. COVID-19 Diagnosis from Chest X-ray Images Using a Robust Multi-Resolution Analysis Siamese Neural Network with Super-Resolution Convolutional Neural Network. Diagnostics 2022;12(3):741 View
  11. Cremades-Martínez P, Parker L, Chilet-Rosell E, Lumbreras B, Jhaveri T. Evaluation of Diagnostic Strategies for Identifying SARS-CoV-2 Infection in Clinical Practice: a Systematic Review and Compliance with the Standards for Reporting Diagnostic Accuracy Studies Guideline (STARD). Microbiology Spectrum 2022;10(4) View
  12. Campagner A, Carobene A, Cabitza F. External validation of Machine Learning models for COVID-19 detection based on Complete Blood Count. Health Information Science and Systems 2021;9(1) View
  13. Babaei Rikan S, Sorayaie Azar A, Ghafari A, Bagherzadeh Mohasefi J, Pirnejad H. COVID-19 diagnosis from routine blood tests using artificial intelligence techniques. Biomedical Signal Processing and Control 2022;72:103263 View
  14. Cardozo G, Tirloni S, Pereira Moro A, Marques J. Use of Artificial Intelligence in the Search for New Information Through Routine Laboratory Tests: Systematic Review. JMIR Bioinformatics and Biotechnology 2022;3(1):e40473 View
  15. Sîrbu A, Barbieri G, Faita F, Ferragina P, Gargani L, Ghiadoni L, Priami C. Early outcome detection for COVID-19 patients. Scientific Reports 2021;11(1) View
  16. Li S, Li M, Wu J, Li Y, Han J, Cao W, Zhou X. Development and validation of a routine blood parameters-based model for screening the occurrence of retinal detachment in high myopia in the context of PPPM. EPMA Journal 2023;14(2):219 View
  17. 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
  18. Rostami M, Oussalah M. A novel explainable COVID-19 diagnosis method by integration of feature selection with random forest. Informatics in Medicine Unlocked 2022;30:100941 View
  19. Douthit B, Walden R, Cato K, Coviak C, Cruz C, D'Agostino F, Forbes T, Gao G, Kapetanovic T, Lee M, Pruinelli L, Schultz M, Wieben A, Jeffery A. Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature. Applied Clinical Informatics 2022;13(01):161 View
  20. Nneji G, Cai J, Monday H, Hossin M, Nahar S, Mgbejime G, Deng J. Fine-Tuned Siamese Network with Modified Enhanced Super-Resolution GAN Plus Based on Low-Quality Chest X-ray Images for COVID-19 Identification. Diagnostics 2022;12(3):717 View
  21. Gürsoy E, Kaya Y. An overview of deep learning techniques for COVID-19 detection: methods, challenges, and future works. Multimedia Systems 2023;29(3):1603 View
  22. 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
  23. 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
  24. Murphy K, Muhairwe J, Schalekamp S, van Ginneken B, Ayakaka I, Mashaete K, Katende B, van Heerden A, Bosman S, Madonsela T, Gonzalez Fernandez L, Signorell A, Bresser M, Reither K, Glass T. COVID-19 screening in low resource settings using artificial intelligence for chest radiographs and point-of-care blood tests. Scientific Reports 2023;13(1) View
  25. 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
  26. Miele L, Dajko M, Savino M, Capocchiano N, Calvez V, Liguori A, Masciocchi C, Vetrone L, Mignini I, Schepis T, Marrone G, Biolato M, Cesario A, Patarnello S, Damiani A, Grieco A, Valentini V, Gasbarrini A. Fib-4 score is able to predict intra-hospital mortality in 4 different SARS-COV2 waves. Internal and Emergency Medicine 2023;18(5):1415 View
  27. Gao T, Ren H, He S, Liang D, Xu Y, Chen K, Wang Y, Zhu Y, Dong H, Xu Z, Chen W, Cheng W, Jing F, Tao X. Development of an interpretable machine learning-based intelligent system of exercise prescription for cardio-oncology preventive care: A study protocol. Frontiers in Cardiovascular Medicine 2023;9 View
  28. Santos-Silva M, Sousa N, Sousa J. Artificial intelligence in routine blood tests. Frontiers in Medical Engineering 2024;2 View
  29. Zhao B, Zhang R, Chen D, Bai K, Zhao H, Gong S, Zhu X. A Machine-Learning-Based Approach for Identifying Diagnostic Errors in Electronic Medical Records. IEEE Transactions on Reliability 2024;73(2):1172 View
  30. Ahmed H. Advanced statistical methods for hazard modeling in cardiothoracic surgery: a comprehensive review of techniques and approaches. Indian Journal of Thoracic and Cardiovascular Surgery 2024;40(5):633 View
  31. Çakı B, Egesoy A, Topaloğlu Y. Makine Öğrenmesi Yöntemleri ile Kan Tahlilinden Covid-19 Teşhisi. Bilgisayar Bilimleri ve Mühendisliği Dergisi 2024;17(2):120 View

Books/Policy Documents

  1. Nayak S, Ganguly C, Gupta A. Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases. View