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

JMIR Theme Issue: COVID-19 Special Issue (2391) Artificial Intelligence (1198) Clinical Information and Decision Making (1328) Decision Support for Health Professionals (1137) Machine Learning (1385) Infectious Diseases (non-STD/STI) (1485) Outbreak and Pandemic Preparedness and Management (1675) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1482)

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 (30) Tweetations (16) Metrics

Journals

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  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
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  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
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  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
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  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
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  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
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Books/Policy Documents

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