Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction
Multicenter validation of a machine-learning algorithm for 48-h all-cause mortality prediction
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DOI：
10.1177/1460458219894494
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发表时间：
2019-12-30
影响因子：
3
通讯作者：
Das, Ritankar
中科院分区：
文献类型：
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作者：
Mohamadlou, Hamid;Panchavati, Saarang;Das, Ritankar
为了评估基于提升树的死亡率预测，这项回顾性研究使用了来自三个学术健康中心的18岁或以上住院患者的电子病历数据，每个生命体征至少有一个观察结果。在死亡前12、24和48小时进行预测。使用来自同一机构和其他机构的保持测试数据对来自每个机构的训练数据拟合的模型进行评估。使用受试者工作特征曲线下面积（AUROC），将免疫增强树（GBT）与正则化逻辑回归（LR）预测、支持向量机（SVM）预测、快速脓毒症相关器官衰竭评估（qSOFA）和改良早期预警评分（MEWS）进行比较。对于来自同一机构的数据的训练和测试GBT，12小时，24小时和48小时预测的机构测试集的平均AUROC分别为0.96，0.95和0.94。当对来自不同医院的数据进行训练和测试时，GBT AUROC在12小时、24小时和48小时的预测分别达到了0.98、0.96和0.96。LR、SVM、MEWS和qSOFA 48小时预测的平均AUROC分别为0.85、0.79、0.86和0.82。GBT预测可能有助于确定谁将受益于增加临床护理的患者。
In order to evaluate mortality predictions based on boosted trees, this retrospective study uses electronic medical record data from three academic health centers for inpatients 18 years or older with at least one observation of each vital sign. Predictions were made 12, 24, and 48 hours before death. Models fit to training data from each institution were evaluated using hold-out test data from the same institution, and from the other institutions. Gradient-boosted trees (GBT) were compared to regularized logistic regression (LR) predictions, support vector machine (SVM) predictions, quick Sepsis-Related Organ Failure Assessment (qSOFA), and Modified Early Warning Score (MEWS) using area under the receiver operating characteristic curve (AUROC). For training and testing GBT on data from the same institution, the average AUROCs were 0.96, 0.95, and 0.94 across institutional test sets for 12-, 24-, and 48-hour predictions, respectively. When trained and tested on data from different hospitals, GBT AUROCs achieved up to 0.98, 0.96, and 0.96, for 12-, 24-, and 48-hour predictions, respectively. Average AUROC for 48-hour predictions for LR, SVM, MEWS, and qSOFA were 0.85, 0.79, 0.86 and 0.82, respectively. GBT predictions may help identify patients who would benefit from increased clinical care.