使用五种机器学习方法预测危重老年结直肠癌 (CRC) 患者的 28 天死亡率。数据从训练队列的 eICU 协作研究数据库 (eICU-CRD)（2.0 版）和重症监护医疗信息集市-IV (MIMIC-IV) 和武汉协和医院进行验证队列。收集临床信息（即人口统计数据、初始实验室检查、生命体征、结果）。应用五种机器学习算法（LightGBM、决策树、XGBoost、随机森林和集成模型）和逻辑回归来预测 28 天死亡率。总体而言，eICU 队列中包括 693 名患者，MIMIC 中包括 181 名患者-IV队列和来自武汉联合队列的95人。在六种机器学习模型中，集成模型在训练队列中表现出最好的预测能力（AUC，0.86），其次是随机森林（AUC，0.83）和LightGBM（AUC，0.82）。该模型还在验证队列中获得了良好的 28 天死亡率预测性能。我们表明，机器学习算法可用于危重、老年 CRC 患者的 28 天死亡率预测。

To predict the 28-day mortality of critically ill, elderly patients with colorectal cancer (CRC) using five machine learning approaches.Data were extracted from the eICU Collaborative Research Database (eICU-CRD) (version 2.0) for a training cohort and from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) and Wuhan Union hospital for validation cohorts. Clinical information (i.e., demographics; initial laboratory tests; vital signs; outcomes) were collected. Five machine learning algorithms (LightGBM, decision tree, XGBoost, random forest, and ensemble model) and a logistic regression were applied for the prediction of 28-day mortality.Overall, 693 patients were included from the eICU cohort, 181 patients from the MIMIC-IV cohort and 95 from the Wuhan Union cohort. Among the six machine learning models, the ensemble model exhibited the best predictive ability (AUC, 0.86), followed by random forest (AUC, 0.83) and LightGBM (AUC, 0.82) in the training cohort. The models also obtained the good predictive performance for the 28-day mortality in the validation cohorts.We showed that machine learning algorithms can be used for the 28-day mortality prediction in critically ill, elderly patients with CRC.