Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal Bleeding

Authors

DOI:

https://doi.org/10.17230/ingciencia.17.34.1

Keywords:

Web design, Machine learning, training, decision trees, weka

Abstract

The evolution of medicine in current times has gone hand in hand with technology where more and more solutions are implemented; those supporting certain medical procedures to serve as base in the field of medical  professionals. The process of analyzing data has become an essential resource in the practice of any profession; currently, in hospitals, more specifically in the university hospital La Samaritana. No tool allows the supporting of diagnosis to determine the supply or no, proton pump inhibitors, therefore we have developed an app using a machine learning model, based on decision trees through the weka application, which, after analyzing the data collected, allows the doctor to count with a tool to support this procedure. We hope that with this, doctors can perform an effective analysis before prescribing or not prescribing PPIs. 

Downloads

Download data is not yet available.

Author Biographies

  • Carlos Calderón-Vargas, La Samaritana Hospital University

    Doctor from the Universidad de la Sabana and specialist in Internal Medicine from the Pontificia Universidad Javeriana. Professor of the Internal Medicine program at the University of La Sabana. Internist of the Internal Medicine service of the Hospital Universitario de la Samaritana. ACMI - ACP member.

  • José Muñoz Castaño, La Samaritana Hospital University

    Doctor and surgeon from the Fundación Universitaria de Ciencias de la Salud, specializing in health management.

  • María Vargas Rincón, La Samaritana Hospital University

    Student XII semester of medicine at the Universidad de la Sabana, I belong to the interest group "Plastic and Reconstructive surgery Universidad de la Sabana" - PRECSUS.

  • Víctor Manuel Rincón Acosta, Universidad El Bosque

    Systems Engineer Student, Diploma in Administrative Skills for the 21st Century, currently works as a leader in the area of technology, computing and robotics at Gimnasio Campestre Reino Británico

  • Miguel Mendieta Hernández, Universidad El Bosque

    Systems engineering student at El Bosque University. He currently works as a Freelance Freelance Web Developer.

References

F. Alshamsi, E. Belley-Cote, D. Cook, S. A. Almenawer, Z. Alqahtani, D. Perri, L. Thabane, A. Al-Omari, K. Lewis, G. Guyatt et al., “Efficacy and safety of proton pump inhibitors for stress ulcer prophylaxis in critically ill patients: a systematic review and meta-analysis of randomized trials,” Critical Care, vol. 20, no. 1, pp. 1–12, 2016. https://doi.org/10.1186/s13054-016-1305-6

J. Johnstone, K. Nerenberg, and M. Loeb, “Meta-analysis: proton pump inhibitor use and the risk of community-acquired pneumonia,” Alimentary pharmacology & therapeutics, vol. 31, no. 11, pp. 1165–1177, 2010. https://doi.org/10.1111/j.1365-2036.2010.04284.x

C.-S. Eom, C. Y. Jeon, J.-W. Lim, E.-G. Cho, S. M. Park, and K.-S. Lee, “Use of acid-suppressive drugs and risk of pneumonia: a systematic review and meta-analysis,” Cmaj, vol. 183, no. 3, pp. 310–319, 2011. https://doi.org/10.1503/cmaj.092129

J. Leonard, J. K. Marshall, and P. Moayyedi, “Systematic review of the risk of enteric infection in patients taking acid suppression,” American Journal of Gastroenterology, vol. 102, no. 9, pp. 2047–2056, 2007. https://doi.org/10.1111/j.1572-0241.2007.01275.x

U. Food, D. Administration et al., “Fda drug safety communication: Clostridium difficile–associated diarrhea can be associated with stomach acid drugs known as proton pump inhibitors (ppis),” Silver Spring, MD: US Food and Drug Administration, 2013.

H. Wang, C. Ma, and L. Zhou, “A brief review of machine learning and its application,” in 2009 international conference on information engineering and computer science. IEEE, 2009, pp. 1–4. https://doi.org/10.1109/ICIECS.2009.5362936

J. M. Moine, A. S. Haedo, and S. E. Gordillo, “Estudio comparativo de metodologías para minería de datos,” in XIII Workshop de Investigadores en Ciencias de la Computación, 2011. http://sedici.unlp.edu.ar/bitstream/handle/10915/20034/Documento_completo.pdf%3Fsequence%3D1

J. A. Sidey-Gibbons and C. J. Sidey-Gibbons, “Machine learning in medicine: a practical introduction,” BMC medical research methodology, vol. 19, no. 1, pp. 1–18, 2019. https://doi.org/10.1186/s12874-019-0681-4

M. Á. Vega, L. M. Q. Mora, M. V. C. Badilla et al., “Inteligencia artificial y aprendizaje automático en medicina,” Revista Medica Sinergia, vol. 5, no. 8, pp. e557–e557, 2020. https://doi.org/10.31434/rms.v5i8.557

W. Hong, Z. Xiong, N. Zheng, and Y. Weng, “A medical-history-based potential disease prediction algorithm,” IEEE Access, vol. 7, pp. 131 094–131 101, 2019. https://doi.org/10.1109/ACCESS.2019.2940644

M. Boehm, D. Burdick, A. Evfimievski, B. Reinwald, P. Sen, S. Tatikonda, and Y. Tian, “Compiling machine learning algorithms with systemml,” in Proceedings of the 4th annual Symposium on Cloud Computing, 2013, pp. 1–1. https://doi.org/10.1145/2523616.2525965

M. M. Kaklamanis and M. E. Filippakis, “A comparative survey of machine learning classification algorithms for breast cancer detection,” in Proceedings of the 23rd Pan-Hellenic Conference on Informatics, 2019, pp. 97–103. https://doi.org/10.1145/3368640.3368642

A. N. Reiz, M. A. de la Hoz, and M. S. García, “Big data analysis y machine learning en medicina intensiva,” Medicina Intensiva, vol. 43, no. 7, pp. 416–426, 2019. https://doi.org/10.1016/j.medin.2018.10.007

S. Chadha, “Clinical oracle: Machine learning in medicine,” Berkeley Scientific Journal, vol. 23, no. 2, 2019. https://doi.org/10.5070/ BS3232045344

Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal Bleeding

Downloads

Published

2021-12-01

Issue

Vol. 17 No. 34 (2021)

Section

Articles

License

Copyright (c) 2021 Miguel Ángel Mendieta Hernández, Víctor Manuel Rincón Acosta, Carlos Mauricio Calderón Vargas, José Fabio Muñoz Castaño, María Alejandra Vargas Rincón

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with this journal agree to the following terms:

  • Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
  • Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
  • Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).

 

How to Cite

Calderón-Vargas, C., Muñoz Castaño, J., Vargas Rincón, M., Rincón Acosta, V. M., & Mendieta Hernández, M. (2021). Deep Learning for Forecast Scales to Prescribe Patients at Risk of Gastrointestinal Bleeding. Ingeniería Y Ciencia, 17(34), 7-22. https://doi.org/10.17230/ingciencia.17.34.1