Efficacy of machine learning algorithms versus conventional assessment techniques in predicting postoperative complications in general surgery: a comprehensive literature review
DOI:
https://doi.org/10.56183/iberojhr.v4is.627Palavras-chave:
machine learning, efficacy, predicting postoperative complications, general surgery, literature review.Resumo
We examine machine learning algorithms' efficacy and core abilities versus conventional methods in predicting postoperative complications in general surgery. Our findings revealed that machine learning algorithms generally supervised and non-supervised assessment techniques in predicting postoperative complications, offering greater accuracy and reliability, thus suggesting a shift towards integrating these advanced tools in clinical practice. This paper discusses the potential of machine learning to revolutionize postoperative care, enhancing prediction accuracy and improving patient outcomes significantly.
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Copyright (c) 2024 María Joaquina Vargas Ladinez, Carla Beatríz Delgado Figueroa, Paola Gissela Placencia Guartatanga, Bryan Andrés Andrade Veloz, Christian Andrés Lascano Arias, Yaonel Fonseca Dominguez
Este trabalho está licenciado sob uma licença Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
