A novel model to label delirium in an intensive care unit from clinician actions.
A novel model to label delirium in an intensive care unit from clinician actions.
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DOI：
10.1186/s12911-021-01461-6
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发表时间：
2021-03-09
影响因子：
3.5
通讯作者：
Fareed N
中科院分区：
文献类型：
--
作者：
Coombes CE;Coombes KR;Fareed N
在重症监护病房（ICU），谵妄是一种常见的急性意识模糊状态，与短期和长期发病率和死亡率的高风险相关。机器学习（ML）有望解决研究重点并改善谵妄结果。然而，由于临床和计费惯例，谵妄通常在电子健康记录（EHR）数据集中标记不一致或不完全。在这里，我们确定了从电子健康记录（EHR）数据中的临床指南中提取的临床行动，这些数据表明重症监护病房（ICU）患者存在谵妄风险。我们开发了一种新的预测模型，根据大数据集标记谵妄患者并评估模型性能。从2001年到2012年，通过重症监护医学信息市场-III数据库（MIMIC-III）获得的48，451例住院患者的EHR数据用于识别特征以开发我们的预测模型。五个二进制ML分类模型（逻辑回归;分类和回归树;随机森林;朴素贝叶斯;和支持向量机）被拟合并通过曲线下面积（AUC）评分进行排名。我们比较了我们的最佳模型与文献中先前提出的两个模型的拟合优度，精度，并通过生物学验证。我们用于预测谵妄的阈值重新分类的最佳性能模型是基于使用31个临床动作的多元逻辑回归（AUC 0.83）。我们的模型通过对临床上有意义的谵妄相关结果的生物学验证，表现出其他提出的模型。在大规模数据集中识别准确标签的障碍限制了ML在谵妄中的临床应用。我们开发了一个新的标签模型谵妄在ICU使用一个大的，公共数据集。通过使用独立于风险因素，治疗和结果的指南指导的临床行动作为模型预测因子，我们的分类器可以用作未来临床目标模型的谵妄标签。在线版本包含补充材料，可通过10.1186/s12911-021-01461-6获得。
In the intensive care unit (ICU), delirium is a common, acute, confusional state associated with high risk for short- and long-term morbidity and mortality. Machine learning (ML) has promise to address research priorities and improve delirium outcomes. However, due to clinical and billing conventions, delirium is often inconsistently or incompletely labeled in electronic health record (EHR) datasets. Here, we identify clinical actions abstracted from clinical guidelines in electronic health records (EHR) data that indicate risk of delirium among intensive care unit (ICU) patients. We develop a novel prediction model to label patients with delirium based on a large data set and assess model performance. EHR data on 48,451 admissions from 2001 to 2012, available through Medical Information Mart for Intensive Care-III database (MIMIC-III), was used to identify features to develop our prediction models. Five binary ML classification models (Logistic Regression; Classification and Regression Trees; Random Forests; Naïve Bayes; and Support Vector Machines) were fit and ranked by Area Under the Curve (AUC) scores. We compared our best model with two models previously proposed in the literature for goodness of fit, precision, and through biological validation. Our best performing model with threshold reclassification for predicting delirium was based on a multiple logistic regression using the 31 clinical actions (AUC 0.83). Our model out performed other proposed models by biological validation on clinically meaningful, delirium-associated outcomes. Hurdles in identifying accurate labels in large-scale datasets limit clinical applications of ML in delirium. We developed a novel labeling model for delirium in the ICU using a large, public data set. By using guideline-directed clinical actions independent from risk factors, treatments, and outcomes as model predictors, our classifier could be used as a delirium label for future clinically targeted models. The online version contains supplementary material available at 10.1186/s12911-021-01461-6.
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影响因子：
39.2
作者：
Collins, Gary S.;Reitsma, Johannes B.;Moons, Karel G. M.
通讯作者：
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DOI：
10.1093/jamia/ocy075
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影响因子：
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