Department of Emergency Medicine, Chi Mei Medical Center, 901 Zhonghua Road, Yongkang District, Tainan City, 710, Taiwan.
Department of Biotechnology, Southern Taiwan University of Science and Technology, Tainan, Taiwan.
BMC Geriatr. 2021 Apr 27;21(1):280. doi: 10.1186/s12877-021-02229-3.
Predicting outcomes in older patients with influenza in the emergency department (ED) by machine learning (ML) has never been implemented. Therefore, we conducted this study to clarify the clinical utility of implementing ML.
We recruited 5508 older ED patients (≥65 years old) in three hospitals between 2009 and 2018. Patients were randomized into a 70%/30% split for model training and testing. Using 10 clinical variables from their electronic health records, a prediction model using the synthetic minority oversampling technique preprocessing algorithm was constructed to predict five outcomes.
The best areas under the curves of predicting outcomes were: random forest model for hospitalization (0.840), pneumonia (0.765), and sepsis or septic shock (0.857), XGBoost for intensive care unit admission (0.902), and logistic regression for in-hospital mortality (0.889) in the testing data. The predictive model was further applied in the hospital information system to assist physicians' decisions in real time.
ML is a promising way to assist physicians in predicting outcomes in older ED patients with influenza in real time. Evaluations of the effectiveness and impact are needed in the future.
通过机器学习 (ML) 预测急诊科 (ED) 老年流感患者的结局从未得到实施。因此，我们开展了这项研究，以明确实施 ML 的临床实用性。
我们招募了 2009 年至 2018 年间三家医院的 5508 名老年 ED 患者（≥65 岁）。患者被随机分为 70%/30%的比例进行模型训练和测试。使用电子病历中的 10 个临床变量，构建了一个使用合成少数过采样技术预处理算法的预测模型，以预测五种结局。
预测结局的最佳曲线下面积为：随机森林模型对住院（0.840）、肺炎（0.765）和脓毒症或感染性休克（0.857）、XGBoost 对重症监护病房入院（0.902）以及逻辑回归对院内死亡率（0.889）的预测在测试数据中表现最佳。该预测模型进一步应用于医院信息系统，以实时协助医生做出决策。
ML 是一种很有前途的方法，可以帮助医生实时预测老年 ED 流感患者的结局。未来需要对其有效性和影响进行评估。
