Classification of Acid-Base Disorders Through Machine Learning-Reference-Cited by-同舟云学术

Classification of Acid-Base Disorders Through Machine Learning

Published:2023-10-26 Issue: Volume: Page:24-31
ISSN:1680-0737
Container-title:IFMBE Proceedings
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Author:

de la Peña Martinez Rodrigo Ruiz^ORCID,Del Val Saucedo Eutzhel André^ORCID,de la Cruz Gallardo Paola Aidee^ORCID,Figueroa Carlos Eduardo Cañedo^ORCID,Pando Abimael Guzmán^ORCID,Lira Natalia Gabriela Sámano^ORCID

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-46933-6_3

Reference10 articles.

1. Lewis, J.L.: III Introducción al equilibrio ácido-básico. Manual MSD Versión Para Público General (2023). https://www.msdmanuals.com/es-mx/hogar/trastornos-hormonales-y-metab%C3%B3licos/equilibrio-%C3%A1cido-b%C3%A1sico/introducci%C3%B3n-al-equilibrio-%C3%A1cido-b%C3%A1sico

2. Del Pilar Triana Reyes, M., Pérez, V.H.E., Durán, D.P.J.: Chapter two - análisis de gases en la sangre. In: Estupiñan, V.H. (Ed.) Bases Para la Interpretación y Análisis de Gases Arteriovenosos. Cali, Colombia: Editorial Universidad Santiago de Cali; 2020, pp. 29–100 (2020). https://doi.org/10.35985/9789585583801

3. Durugkar, S.R., Raja, R., Nagwanshi, K.K., Kumar, S.: Introduction to data mining. Data Min. Mach. Learn. Appl. 1−19. https://doi.org/10.1002/9781119792529.ch1

4. Malekian, A., Chitsaz, N.: Concepts, procedures, and applications of artificial neural network models in streamflow forecasting. In: Advances in Streamflow Forecasting, pp. 115−147. Elsevier (2021). https://doi.org/10.1016/b978-0-12-820673-7.00003-2

5. Shehab, M., et al.: Chapter eight - artificial neural networks for engineering applications: a review. In: Elsheikh, A.H., Elaziz, M.E.A. (Eds.) Artificial Neural Networks for Renewable Energy Systems and Real World Applications, pp. 189–206. Academic Press (2022). ISBN 9780128207932. https://doi.org/10.1016/B978-0-12-820793-2.00003-3