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Stocker M, Daunhawer I, van Herk W, et al. Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis. Pediatr Infect Dis J. 2021;doi: 10.1097/INF.0000000000003344.
Stocker, M., Daunhawer, I., van Herk, W., El Helou, S., Dutta, S., Schuerman, F. A. B. A., van den Tooren-de Groot, R. K., Wieringa, J. W., Janota, J., van der Meer-Kappelle, L. H., Moonen, R., Sie, S. D., de Vries, E., Donker, A. E., Zimmerman, U., Schlapbach, L. J., de Mol, A. C., Hoffmann-Haringsma, A., Roy, M., Tomaske, M., Kornelisse, R. F., van Gijsel, J., Plötz, F. B., Wellmann, S., Achten, N. B., Lehnick, D., van Rossum, A. M. C., & Vogt, J. E. (2021). Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis. The Pediatric infectious disease journal, . https://doi.org/10.1097/INF.0000000000003344
Stocker, Martin, et al. "Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis." The Pediatric infectious disease journal vol. (2021). doi: https://doi.org/10.1097/INF.0000000000003344
Stocker M, Daunhawer I, van Herk W, El Helou S, Dutta S, Schuerman FABA, van den Tooren-de Groot RK, Wieringa JW, Janota J, van der Meer-Kappelle LH, Moonen R, Sie SD, de Vries E, Donker AE, Zimmerman U, Schlapbach LJ, de Mol AC, Hoffmann-Haringsma A, Roy M, Tomaske M, Kornelisse RF, van Gijsel J, Plötz FB, Wellmann S, Achten NB, Lehnick D, van Rossum AMC, Vogt JE. Machine Learning Used to Compare the Diagnostic Accuracy of Risk Factors, Clinical Signs and Biomarkers and to Develop a New Prediction Model for Neonatal Early-onset Sepsis. Pediatr Infect Dis J. 2021 Sep 09; doi: 10.1097/INF.0000000000003344. Epub 2021 Sep 09. PMID: 34508027.
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