Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo, Japan.
Artificial Intelligence in Healthcare, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
PLoS One. 2021 Feb 5;16(2):e0246640. doi: 10.1371/journal.pone.0246640. eCollection 2021.
Risk assessment of in-hospital mortality of patients at the time of hospitalization is necessary for determining the scale of required medical resources for the patient depending on the patient's severity. Because recent machine learning application in the clinical area has been shown to enhance prediction ability, applying this technique to this issue can lead to an accurate prediction model for in-hospital mortality prediction. In this study, we aimed to generate an accurate prediction model of in-hospital mortality using machine learning techniques. Patients 18 years of age or older admitted to the University of Tokyo Hospital between January 1, 2009 and December 26, 2017 were used in this study. The data were divided into a training/validation data set (n = 119,160) and a test data set (n = 33,970) according to the time of admission. The prediction target of the model was the in-hospital mortality within 14 days. To generate the prediction model, 25 variables (age, sex, 21 laboratory test items, length of stay, and mortality) were used to predict in-hospital mortality. Logistic regression, random forests, multilayer perceptron, and gradient boost decision trees were performed to generate the prediction models. To evaluate the prediction capability of the model, the model was tested using a test data set. Mean probabilities obtained from trained models with five-fold cross-validation were used to calculate the area under the receiver operating characteristic (AUROC) curve. In a test stage using the test data set, prediction models of in-hospital mortality within 14 days showed AUROC values of 0.936, 0.942, 0.942, and 0.938 for logistic regression, random forests, multilayer perceptron, and gradient boosting decision trees, respectively. Machine learning-based prediction of short-term in-hospital mortality using admission laboratory data showed outstanding prediction capability and, therefore, has the potential to be useful for the risk assessment of patients at the time of hospitalization.
对住院患者的住院期间死亡率进行风险评估，对于根据患者的严重程度确定患者所需医疗资源的规模是必要的。由于最近在临床领域应用的机器学习已被证明可以提高预测能力，因此将该技术应用于该问题可以为住院死亡率预测生成准确的预测模型。在这项研究中，我们旨在使用机器学习技术生成住院死亡率的准确预测模型。该研究使用了 2009 年 1 月 1 日至 2017 年 12 月 26 日期间入住东京大学医院的 18 岁或以上的患者。根据入院时间，将数据分为训练/验证数据集（n=119160）和测试数据集（n=33970）。该模型的预测目标是 14 天内的住院死亡率。为了生成预测模型，使用了 25 个变量（年龄、性别、21 项实验室检查项目、住院时间和死亡率）来预测住院死亡率。进行了逻辑回归、随机森林、多层感知机和梯度提升决策树，以生成预测模型。为了评估模型的预测能力，使用测试数据集对模型进行了测试。使用五重交叉验证从训练模型中获得的平均概率用于计算接收器操作特性（AUROC）曲线下的面积。在使用测试数据集的测试阶段，住院 14 天内死亡率的预测模型的 AUROC 值分别为逻辑回归、随机森林、多层感知机和梯度提升决策树的 0.936、0.942、0.942 和 0.938。基于机器学习的入院实验室数据短期住院死亡率预测具有出色的预测能力，因此有可能用于住院患者的风险评估。
