【摘要】 目的 构建机器学习模型预测重症监护病房(ICU)患者全因死亡的风险。方法 基于重症监护医疗信息市场Ⅲ(MIMIC-Ⅲ)数据库，使用机器学习法将电子病历(EHR)中的结构化和非结构化数据相整合，创建ICU患者的死亡风险预测模型。结果 结合结构化和非结构化数据的机器学习模型提高了ICU患者临床结局预测的准确性，优选出的梯度增强模型的受试者操作特征曲线下面积(AUROC)值为0.88,表明可以准确预测患者生命状态。结论 采用机器学习模型，基于少量易于收集的结构化变量结合非结构化数据，可以显著提高ICU患者死亡风险预测模型的预测性能。更多还原

【Abstract】 Objective To construct a machine learning model to predict the risk of all-cause mortality in intensive care unit(ICU) patients.Methods Based on the intensive care medical information market Ⅲ(MIMIC-Ⅲ) database, the machine learning method was used to integrate the structured and unstructured data in the electronic medical record(EHR) to create a mortality risk prediction model for ICU patients.Results The machine learning model combined with structured and unstructured data improved the accuracy of clinical outcome prediction of ICU patients.The AUROC value of the optimized gradient enhancement model was 0.88,indicating that the patient′s life state could be accurately predicted.Conclusion Using machine learning models, based on a small number of easily collected structured variables combined with unstructured data, can significantly improve the prediction performance of ICU patients′ mortality risk prediction models.更多还原