使用机器学习预测72小时和9天内返回急诊科的情况。
Predicting 72-hour and 9-day return to the emergency department using machine learning.
作者信息
Hong Woo Suk, Haimovich Adrian Daniel, Taylor Richard Andrew
机构信息
Yale School of Medicine, New Haven, Connecticut, USA.
Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, USA.
出版信息
JAMIA Open. 2019 Jul 1;2(3):346-352. doi: 10.1093/jamiaopen/ooz019. eCollection 2019 Oct.
OBJECTIVES
To predict 72-h and 9-day emergency department (ED) return by using gradient boosting on an expansive set of clinical variables from the electronic health record.
METHODS
This retrospective study included all adult discharges from a level 1 trauma center ED and a community hospital ED covering the period of March 2013 to July 2017. A total of 1500 variables were extracted for each visit, and samples split randomly into training, validation, and test sets (80%, 10%, and 10%). Gradient boosting models were fit on 3 selections of the data: administrative data (demographics, prior hospital usage, and comorbidity categories), data available at triage, and the full set of data available at discharge. A logistic regression (LR) model built on administrative data was used for baseline comparison. Finally, the top 20 most informative variables identified from the full gradient boosting models were used to build a reduced model for each outcome.
RESULTS
A total of 330 631 discharges were available for analysis, with 29 058 discharges (8.8%) resulting in 72-h return and 52 748 discharges (16.0%) resulting in 9-day return to either ED. LR models using administrative data yielded test AUCs of 0.69 (95% confidence interval [CI] 0.68-0.70) and 0.71(95% CI 0.70-0.72), while gradient boosting models using administrative data yielded test AUCs of 0.73 (95% CI 0.72-0.74) and 0.74 (95% CI 0.73-0.74) for 72-h and 9-day return, respectively. Gradient boosting models using variables available at triage yielded test AUCs of 0.75 (95% CI 0.74-0.76) and 0.75 (95% CI 0.74-0.75), while those using the full set of variables yielded test AUCs of 0.76 (95% CI 0.75-0.77) and 0.75 (95% CI 0.75-0.76). Reduced models using the top 20 variables yielded test AUCs of 0.73 (95% CI 0.71-0.74) and 0.73 (95% CI 0.72-0.74).
DISCUSSION AND CONCLUSION
Gradient boosting models leveraging clinical data are superior to LR models built on administrative data at predicting 72-h and 9-day returns.
目的
通过对电子健康记录中大量临床变量使用梯度提升算法，预测72小时和9天内急诊室（ED）复诊情况。
方法
这项回顾性研究纳入了2013年3月至2017年7月期间，一级创伤中心急诊室和社区医院急诊室的所有成年出院患者。每次就诊提取了总共1500个变量，并将样本随机分为训练集、验证集和测试集（分别为80%、10%和10%）。梯度提升模型基于3种数据选择进行拟合：管理数据（人口统计学、既往住院情况和合并症类别）、分诊时可用数据以及出院时可用的完整数据集。基于管理数据构建的逻辑回归（LR）模型用于基线比较。最后，从完整的梯度提升模型中确定的前20个最具信息量的变量用于为每个结果构建简化模型。
结果
共有330631例出院患者可供分析，其中29058例（8.8%）在72小时内复诊，52748例（16.0%）在9天内返回急诊室。使用管理数据的LR模型在72小时和9天复诊预测中的测试AUC分别为0.69（95%置信区间[CI]0.68 - 0.70）和0.71（95%CI 0.70 - 0.72），而使用管理数据的梯度提升模型在72小时和9天复诊预测中的测试AUC分别为0.73（95%CI 0.72 - 0.74）和0.74（95%CI 0.73 - 0.74）。使用分诊时可用变量的梯度提升模型在72小时和9天复诊预测中的测试AUC分别为0.75（95%CI 0.74 - 0.76）和0.75（95%CI 0.74 - 0.75），而使用完整变量集的模型在72小时和9天复诊预测中的测试AUC分别为0.76（95%CI 0.75 - 0.77）和0.75（95%CI 0.75 - 0.76）。使用前20个变量的简化模型在72小时和9天复诊预测中的测试AUC分别为0.73（95%CI 0.71 - 0.74）和0.73（95%CI 0.72 - 0.74）。
讨论与结论
利用临床数据的梯度提升模型在预测72小时和9天复诊方面优于基于管理数据构建的LR模型。
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