APA (7th ed.) Citation

Pfaff, E. R., Girvin, A. T., Crosskey, M., Gangireddy, S., Master, H., Wei, W., . . . Consortia, N. a. R. (2023). De-black-boxing health AI: Demonstrating reproducible machine learning computable phenotypes using the N3C-RECOVER Long COVID model in the All of Us data repository. Journal of the American Medical Informatics Association, 30(7), 1305. https://doi.org/10.1093/jamia/ocad077

Chicago Style (17th ed.) Citation

Pfaff, Emily R., et al. "De-black-boxing Health AI: Demonstrating Reproducible Machine Learning Computable Phenotypes Using the N3C-RECOVER Long COVID Model in the All of Us Data Repository." Journal of the American Medical Informatics Association 30, no. 7 (2023): 1305. https://doi.org/10.1093/jamia/ocad077.

MLA (9th ed.) Citation

Pfaff, Emily R., et al. "De-black-boxing Health AI: Demonstrating Reproducible Machine Learning Computable Phenotypes Using the N3C-RECOVER Long COVID Model in the All of Us Data Repository." Journal of the American Medical Informatics Association, vol. 30, no. 7, 2023, p. 1305, https://doi.org/10.1093/jamia/ocad077.