Machine learning can be considered as the current gold standard for predicting deterioration in Intensive Care Unit patients and is in extensive use throughout the world in different fields. As confirmed by many studies, preventing the occurrence of the onset of deterioration in a sufficient time window is a priority in healthcare centers. Also, the significance of enhancing the quality of hospital care and the reduction of adverse outcomes is of great importance. Notably, it is hypothesized that by exploiting recent technologies, models built upon dynamic variables (e.g. vital signs, lab tests, and demographic variables) could reinforce the predictive ability of models aimed at detection of in clinical deterioration with high accuracy, sensitivity and specificity. This manuscript summarises the techniques and approaches proposed in the literature for predicting deterioration and compares the performance and limitations of various approaches grouped based on their application. While several approaches can attain promising results, there is still room for additional improvement, especially in pre-processing and modeling enhancement steps where most methods do not take the necessary steps for ensuring a high-performance result. In this manuscript, the most effective machine learning models, as well as deep learning models, for predicting deterioration of patients are discussed in hopes of assisting the readers with ascertaining the best possible solutions for this problem.

机器学习可以被认为是目前预测重症监护病房患者病情恶化的金标准，并且在世界各地的不同领域得到了广泛的应用。正如许多研究所证实的那样，在足够的时间范围内防止恶化的发生是医疗中心的当务之急。同样，提高医院护理质量和减少不良后果的重要性也很重要。值得注意的是，假设通过利用最新技术，基于动态变量（例如生命体征，实验室检查和人口统计学变量）构建的模型可以增强旨在以高精度，敏感性和特异性检测临床恶化的模型的预测能力。该手稿总结了文献中提出的用于预测劣化的技术和方法，并比较了根据其应用分组的各种方法的性能和局限性。尽管几种方法可以取得可喜的结果，但仍有进一步改进的余地，尤其是在预处理和建模增强步骤中，其中大多数方法未采取确保高性能结果的必要步骤。在本手稿中，讨论了用于预测患者病情恶化的最有效的机器学习模型以及深度学习模型，以期帮助读者确定针对此问题的最佳解决方案。尽管几种方法可以取得可喜的结果，但仍有进一步改进的余地，尤其是在预处理和建模增强步骤中，其中大多数方法未采取确保高性能结果的必要步骤。在本手稿中，讨论了用于预测患者病情恶化的最有效的机器学习模型以及深度学习模型，以期帮助读者确定针对此问题的最佳解决方案。尽管有几种方法可以取得令人满意的结果，但仍有改进的余地，尤其是在预处理和建模增强步骤中，其中大多数方法没有采取必要的步骤来确保获得高性能的结果。在本手稿中，讨论了用于预测患者病情恶化的最有效的机器学习模型以及深度学习模型，以期帮助读者确定针对此问题的最佳解决方案。