The diffusion of electronic health records collecting large amount of clinical, monitoring, and laboratory data produced by intensive care units (ICUs) is the natural terrain for the application of artificial intelligence (AI). AI has a broad definition, encompassing computer vision, natural language processing, and machine learning, with the latter being more commonly employed in the ICUs. Machine learning may be divided in supervised learning models (i.e., support vector machine [SVM] and random forest), unsupervised models (i.e., neural networks [NN]), and reinforcement learning. Supervised models require labeled data that is data mapped by human judgment against predefined categories. Unsupervised models, on the contrary, can be used to obtain reliable predictions even without labeled data. Machine learning models have been used in ICU to predict pathologies such as acute kidney injury, detect symptoms, including delirium, and propose therapeutic actions (vasopressors and fluids in sepsis). In the future, AI will be increasingly used in ICU, due to the increasing quality and quantity of available data. Accordingly, the ICU team will benefit from models with high accuracy that will be used for both research purposes and clinical practice. These models will be also the foundation of future decision support system (DSS), which will help the ICU team to visualize and analyze huge amounts of information. We plea for the creation of a standardization of a core group of data between different electronic health record systems, using a common dictionary for data labeling, which could greatly simplify sharing and merging of data from different centers.

收集重症监护病房 (ICU) 产生的大量临床、监测和实验室数据的电子健康记录的传播是人工智能 (AI) 应用的自然领域。人工智能有一个广泛的定义，包括计算机视觉、自然语言处理和机器学习，后者更常用于 ICU。机器学习可以分为监督学习模型（即支持向量机[SVM]和随机森林）、无监督模型（即神经网络[NN]）和强化学习。监督模型需要标记数据，这些数据是通过人类判断映射到预定义类别的数据。相反，即使没有标记数据，无监督模型也可用于获得可靠的预测。机器学习模型已在 ICU 中用于预测急性肾损伤等疾病，检测包括谵妄在内的症状，并提出治疗措施（败血症中的升压药和补液）。未来，由于可用数据的质量和数量不断增加，人工智能将越来越多地用于 ICU。因此，ICU 团队将受益于用于研究和临床实践的高精度模型。这些模型也将成为未来决策支持系统 (DSS) 的基础，它将帮助 ICU 团队可视化和分析大量信息。我们呼吁在不同的电子健康记录系统之间创建一组核心数据的标准化，使用通用字典进行数据标记，