Division of Hematology and Medical Oncology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, South Korea.
Department of Biomedical Engineering, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin, 17104, South Korea.
Sci Rep. 2024 May 20;14(1):11503. doi: 10.1038/s41598-024-61627-w.
This study aimed to present a new approach to predict to delirium admitted to the acute palliative care unit. To achieve this, this study employed machine learning model to predict delirium in patients in palliative care and identified the significant features that influenced the model. A multicenter, patient-based registry cohort study in South Korea between January 1, 2019, and December 31, 2020. Delirium was identified by reviewing the medical records based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The study dataset included 165 patients with delirium among 2314 patients with advanced cancer admitted to the acute palliative care unit. Seven machine learning models, including extreme gradient boosting, adaptive boosting, gradient boosting, light gradient boosting, logistic regression, support vector machine, and random forest, were evaluated to predict delirium in patients with advanced cancer admitted to the acute palliative care unit. An ensemble approach was adopted to determine the optimal model. For k-fold cross-validation, the combination of extreme gradient boosting and random forest provided the best performance, achieving the following accuracy metrics: 68.83% sensitivity, 70.85% specificity, 69.84% balanced accuracy, and 74.55% area under the receiver operating characteristic curve. The performance of the isolated testing dataset was also validated, and the machine learning model was successfully deployed on a public website ( http://ai-wm.khu.ac.kr/Delirium/ ) to provide public access to delirium prediction results in patients with advanced cancer. Furthermore, using feature importance analysis, sex was determined to be the top contributor in predicting delirium, followed by a history of delirium, chemotherapy, smoking status, alcohol consumption, and living with family. Based on a large-scale, multicenter, patient-based registry cohort, a machine learning prediction model for delirium in patients with advanced cancer was developed in South Korea. We believe that this model will assist healthcare providers in treating patients with delirium and advanced cancer.
本研究旨在提出一种新方法来预测入住急性姑息治疗病房的谵妄患者。为此，本研究采用机器学习模型预测姑息治疗患者的谵妄，并确定影响模型的显著特征。这是一项 2019 年 1 月 1 日至 2020 年 12 月 31 日期间在韩国进行的多中心、基于患者的注册队列研究。谵妄是根据《精神障碍诊断与统计手册》第五版的标准通过审查病历来确定的。研究数据集包括 2314 名入住急性姑息治疗病房的晚期癌症患者中的 165 名谵妄患者。评估了七种机器学习模型，包括极端梯度提升、自适应提升、梯度提升、轻梯度提升、逻辑回归、支持向量机和随机森林，以预测入住急性姑息治疗病房的晚期癌症患者的谵妄。采用集成方法确定最佳模型。对于 k 折交叉验证，极端梯度提升和随机森林的组合提供了最佳性能，达到以下准确性指标：68.83%的敏感性、70.85%的特异性、69.84%的平衡准确性和 74.55%的接收器操作特征曲线下面积。还验证了独立测试数据集的性能，并成功将机器学习模型部署在一个公共网站（http://ai-wm.khu.ac.kr/Delirium/）上，以便公众可以访问晚期癌症患者的谵妄预测结果。此外，通过特征重要性分析，确定性别是预测谵妄的最重要因素，其次是谵妄史、化疗、吸烟状况、饮酒状况和与家人同住。基于一项大规模、多中心、基于患者的注册队列研究，韩国开发了一种用于预测晚期癌症患者谵妄的机器学习预测模型。我们相信，该模型将有助于医疗保健提供者治疗谵妄和晚期癌症患者。
