Machine learning for the extraction of biomedical information in a clinical laboratory
Abstract
The objective is to apply Machine learning models to find patterns and hidden biomedical information to improve decision making and aid clinical diagnosis, from a rationalistic approach to research. The Knwoledge Discovery in Databases (KDD) process was used to discover and extract knowledge, since it is iterative and interactive. Being an iterative process at each step means that it may be necessary to go back to previous steps. Supervised and unsupervised algorithms have the ability to aid in decision making because it allows for better understanding of the data and can help uncover new questions that can lead to further vital research. Association rules can help to determine factors that influence the health of humans and with this, preventive measures can be taken to improve health status.
Downloads
References
Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255 LP – 260. https://doi.org/10.1126/science.aaa8415
Zou, Q., Qu, K., Luo, Y., Yin, D., Ju, Y., & Tang, H. (2018). Predicting Diabetes Mellitus With Machine Learning Techniques. Frontiers in Genetics, 9, 515. https://doi.org/10.3389/fgene.2018.00515
Braulio Gil, N., & Curto Díaz, J. (2016). Customer analytics. Barcelona: Editorial UOC, S.L.
Hutton, J. (2012). Pediatric Biomedical Informatics: Computer Applications in Pediatric Research (1st ed.). New York: Springer Science & Business Media.
Torres, J. (2018). DEEP LEARNING Introducción práctica con Keras. (I. Published, Ed.) (3rd ed.). Barcelona.
Oded, M., & Lior, R. (2014). Data Mining With Decision Trees: Theory And Applications (2nd ed.). London: World Scientific Publishing Company.
Kononenko, I., & Kukar, M. (2007). Machine Learning and Data Mining (1st ed.). Chichester,Uk: Elsevier Science.
Ahlemeyer-Stubbe, A., & Coleman, S. (2014). A Practical Guide to Data Mining for Business and Industry. https://doi.org/10.1002/9781118763704
Holzinger, A. (2016). Interactive machine learning for health informatics: when do we need the human-in-the-loop? Brain Informatics, 3(2), 119–131. https://doi.org/10.1007/s40708-016-0042-6
Castrillón, O. D., & Sarache, W. (2017). Sistema Bayesiano para la Predicción de la Diabetes Bayesian System for Diabetes Prediction, 28, 161–168.
Luo, Y., Szolovits, P., Baron, J. M., & Dighe, A. S. (2016). Using Machine Learning to Predict Laboratory Test Results. American Journal of Clinical Pathology, 145(6), 778–788. https://doi.org/10.1093/ajcp/aqw064
Copyright (c) 2022 Ivan Mauricio Álvarez-Bonilla, Ariel José Romero-Fernández, Gustavo Eduardo Fernández-Villacrés, Luis Rafael Freire-Lescano
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
CC BY-NC-SA : Esta licencia permite a los reutilizadores distribuir, remezclar, adaptar y construir sobre el material en cualquier medio o formato solo con fines no comerciales, y solo siempre y cuando se dé la atribución al creador. Si remezcla, adapta o construye sobre el material, debe licenciar el material modificado bajo términos idénticos.
OAI-PMH URL: https://cienciamatriarevista.org.ve/index.php/cm/oai
