Machine learning models trained on electronic health records have achieved high prognostic accuracy in test datasets, but little is known about their embedding into clinical workflows. We implemented a random forest–based algorithm to identify hospitalized patients at high risk for delirium, and evaluated its performance in a clinical setting.

中文翻译：

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使用机器学习对住院患者谵妄的风险预测：实施和前瞻性评估研究。

在电子健康记录上训练的机器学习模型在测试数据集中实现了很高的预后准确性，但对其嵌入临床工作流程知之甚少。我们实施了一种基于随机森林的算法来识别谵妄高风险住院患者，并评估其在临床环境中的表现。