Intubation for mechanical ventilation (MV) is one of the most common high-risk procedures performed in Intensive Care Units (ICUs). Early prediction of intubation may have a positive impact by providing timely alerts to clinicians and consequently avoiding high-risk late intubations. In this work, we propose a new machine learning method to predict the time to intubation during the first five days of ICU admission, based on the concept of cure survival models. Our approach combines classification and survival analysis, to effectively accommodate the fraction of patients not at risk of intubation, and provide a better estimate of time to intubation, for patients at risk. We tested our approach and compared it to other predictive models on a dataset collected from a secondary care hospital (AZ Groeninge, Kortrijk, Belgium) from 2015 to 2021, consisting of 3425 ICU stays. Furthermore, we utilised SHAP for feature importance analysis, extracting key insights into the relative significance of variables such as vital signs, blood gases, and patient characteristics in predicting intubation in ICU settings. The results corroborate that our approach improves the prediction of time to intubation in critically ill patients, by using routinely collected data within the first hours of admission in the ICU. Early warning of the need for intubation may be used to help clinicians predict the risk of intubation and rank patients according to their expected time to intubation.

中文翻译：

---

使用机器学习和治愈分数信息预测重症监护入院后的插管时间

机械通气 (MV) 插管是重症监护病房 (ICU) 中最常见的高风险操作之一。早期预测插管可能会产生积极影响，向临床医生提供及时警报，从而避免高风险的晚期插管。在这项工作中，我们基于治愈生存模型的概念，提出了一种新的机器学习方法来预测 ICU 入住前五天的插管时间。我们的方法结合了分类和生存分析，以有效地适应没有插管风险的患者比例，并为有风险的患者提供更好的插管时间估计。我们测试了我们的方法，并将其与其他预测模型进行了比较，该数据集是从 2015 年至 2021 年一家二级护理医院（比利时科特赖克的 AZ Groeninge）收集的，包含 3425 名 ICU 住院患者。此外，我们利用 SHAP 进行特征重要性分析，提取生命体征、血气和患者特征等变量的相对重要性的关键见解，以预测 ICU 环境中的插管。结果证实，我们的方法通过使用 ICU 入院后最初几个小时内常规收集的数据，改善了危重患者插管时间的预测。需要插管的早期预警可用于帮助临床医生预测插管的风险，并根据患者的预期插管时间对患者进行排名。