Using machine learning to improve the diagnostic accuracy of the modified Duke/ESC 2015 criteria in patients with suspected prosthetic valve endocarditis – a proof of concept study.

MLA

ten Hove, D., et al. “Using Machine Learning to Improve the Diagnostic Accuracy of the Modified Duke/ESC 2015 Criteria in Patients with Suspected Prosthetic Valve Endocarditis – a Proof of Concept Study.” European Journal of Nuclear Medicine & Molecular Imaging, vol. 51, no. 13, Nov. 2024, pp. 3924–33. EBSCOhost, https://doi.org/10.1007/s00259-024-06774-y.

APA

ten Hove, D., Slart, R. H. J. A., Glaudemans, A. W. J. M., Postma, D. F., Gomes, A., Swart, L. E., Tanis, W., Geel, P. P. van, Mecozzi, G., Budde, R. P. J., Mouridsen, K., & Sinha, B. (2024). Using machine learning to improve the diagnostic accuracy of the modified Duke/ESC 2015 criteria in patients with suspected prosthetic valve endocarditis – a proof of concept study. European Journal of Nuclear Medicine & Molecular Imaging, 51(13), 3924–3933. https://doi.org/10.1007/s00259-024-06774-y

Chicago

ten Hove, D., R. H. J. A. Slart, A. W. J. M. Glaudemans, D. F. Postma, A. Gomes, L. E. Swart, W. Tanis, et al. 2024. “Using Machine Learning to Improve the Diagnostic Accuracy of the Modified Duke/ESC 2015 Criteria in Patients with Suspected Prosthetic Valve Endocarditis – a Proof of Concept Study.” European Journal of Nuclear Medicine & Molecular Imaging 51 (13): 3924–33. doi:10.1007/s00259-024-06774-y.