Prediction of Incident Delirium Using a Random Forest classifier
Prediction of Incident Delirium Using a Random Forest classifier
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DOI：
10.1007/s10916-018-1109-0
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发表时间：
2018-12-01
影响因子：
5.3
通讯作者：
Dicks, Robert S.
中科院分区：
文献类型：
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作者：
Corradi, John P.;Thompson, Stephen;Dicks, Robert S.
精神错乱是一种严重的内科并发症，预后不佳。鉴于该综合征的复杂性，预防和早期发现是减轻其影响的关键。我们使用混淆评估方法(CAM)筛查和电子健康记录(EHR)数据对64,038例住院患者进行训练和测试，以预测在医院出现的精神错乱。偶发精神错乱被定义为在住院至少48小时后首次出现阳性的CAM。随机森林机器学习算法用于人口统计数据、合并症、药物、程序和生理测量。数据集被随机划分为80%/20%，分别用于训练和验证预测模型。在训练组的51240名患者中，2774名(5.4%)在住院期间经历了精神错乱；在验证组的12798名患者中，701名(5.5%)经历了精神错乱。对精神错乱阴性人群的抽样不足被用来解决阶级不平衡问题。随机森林预测模型的受试者工作特征曲线(ROCAUC)下面积为0.909(95%可信区间为0.898至0.921)。模型中的重要变量包括先前确定的易感和诱发风险因素。这种机器学习方法显示了高度的准确性，并有可能为那些有最大风险发展成妄想的患者的早期干预提供临床上有用的预测模型。
Delirium is a serious medical complication associated with poor outcomes. Given the complexity of the syndrome, prevention and early detection are critical in mitigating its effects. We used Confusion Assessment Method (CAM) screening and Electronic Health Record (EHR) data for 64,038 inpatient visits to train and test a model predicting delirium arising in hospital. Incident delirium was defined as the first instance of a positive CAM occurring at least 48h into a hospital stay. A Random Forest machine learning algorithm was used with demographic data, comorbidities, medications, procedures, and physiological measures. The data set was randomly partitioned 80% / 20% for training and validating the predictive model, respectively. Of the 51,240 patients in the training set, 2774 (5.4%) experienced delirium during their hospital stay; and of the 12,798 patients in the validation set, 701 (5.5%) experienced delirium. Under-sampling of the delirium negative population was used to address the class imbalance. The Random Forest predictive model yielded an area under the receiver operating characteristic curve (ROC AUC) of 0.909 (95% CI 0.898 to 0.921). Important variables in the model included previously identified predisposing and precipitating risk factors. This machine learning approach displayed a high degree of accuracy and has the potential to provide a clinically useful predictive model for earlier intervention in those patients at greatest risk of developing delirium.