ESEI - Escuela Superior de Ingeniería Informática, Universidad de Vigo, Ourense, Spain.
Intensive Care Unit, Complexo Hospitalario Universitario de Ourense, Ourense, Spain.
Med Intensiva (Engl Ed). 2022 Mar;46(3):140-156. doi: 10.1016/j.medine.2020.04.015.
Sepsis is a major public health problem and a leading cause of death in the world, where delay in the beginning of treatment, along with clinical guidelines non-adherence have been proved to be associated with higher mortality. Machine Learning is increasingly being adopted in developing innovative Clinical Decision Support Systems in many areas of medicine, showing a great potential for automatic prediction of diverse patient conditions, as well as assistance in clinical decision making. In this context, this work conducts a narrative review to provide an overview of how specific Machine Learning techniques can be used to improve sepsis management, discussing the main tasks addressed, the most popular methods and techniques, as well as the obtained results, in terms of both intelligent system accuracy and clinical outcomes improvement.
脓毒症是一个主要的公共卫生问题，也是世界范围内的主要死亡原因，治疗开始的延迟以及临床指南的不遵守已被证明与更高的死亡率有关。机器学习在开发许多医学领域的创新临床决策支持系统方面越来越受到关注，它在自动预测各种患者病情以及协助临床决策方面显示出巨大的潜力。在这种情况下，本工作进行了叙述性综述，以概述特定的机器学习技术如何用于改善脓毒症管理，讨论所涉及的主要任务、最流行的方法和技术，以及在智能系统准确性和临床结果改善方面的结果。
