Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events
Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events
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DOI：
10.1093/jamia/ocab111
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发表时间：
2021-07-16
影响因子：
6.4
通讯作者：
Rossetti, Sarah Collins
中科院分区：
文献类型：
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作者：
Fu, Li-Heng;Knaplund, Chris;Rossetti, Sarah Collins
目的：提出一种仅利用纵向电子健康记录数据的时间戳对临床恶化事件进行分类的算法。 材料和方法：这项回顾性研究探讨了机器学习算法使用生命体征测量时间戳序列、流程图注释、医嘱条目和护理笔记对重症监护病房患者的临床恶化事件进行分类的功效。我们设计了一个数据管道，将事件划分为离散的、规则的时间段，我们将其称为时间步长。逻辑回归、随机森林分类器和循环神经网络分别在不同时间步长度的数据集上进行训练，针对死亡、心脏骤停和快速反应小组呼叫的复合结果。然后，这些模型在保留数据集上进行验证。结果：总共 6720 次重症监护病房病例符合标准，最终数据集包含 830 578 个时间戳。门控复发单元模型利用生命体征、订单条目、流程图注释和护理笔记的时间戳，在结果时间数据集上实现最佳性能，精确回忆曲线下面积为 0.101 (0.06, 0.137)，灵敏度为 0.443，阈值为 0.6 时阳性预测值为 0. 092。 讨论和结论：本研究表明，我们的复发单元模型仅使用反映医疗保健流程的纵向电子健康记录数据时间戳的神经网络模型实现了良好的判别能力。
Objective: To propose an algorithm that utilizes only timestamps of longitudinal electronic health record data to classify clinical deterioration events.Materials and methods: This retrospective study explores the efficacy of machine learning algorithms in classifying clinical deterioration events among patients in intensive care units using sequences of timestamps of vital sign measurements, flowsheets comments, order entries, and nursing notes. We design a data pipeline to partition events into discrete, regular time bins that we refer to as timesteps. Logistic regressions, random forest classifiers, and recurrent neural networks are trained on datasets of different length of timesteps, respectively, against a composite outcome of death, cardiac arrest, and Rapid Response Team calls. Then these models are validated on a holdout dataset.Results: A total of 6720 intensive care unit encounters meet the criteria and the final dataset includes 830 578 timestamps. The gated recurrent unit model utilizes timestamps of vital signs, order entries, flowsheet comments, and nursing notes to achieve the best performance on the time-to-outcome dataset, with an area under the precision-recall curve of 0.101 (0.06, 0.137), a sensitivity of 0.443, and a positive predictive value of 0. 092 at the threshold of 0.6.Discussion and Conclusion: This study demonstrates that our recurrent neural network models using only timestamps of longitudinal electronic health record data that reflect healthcare processes achieve well-performing discriminative power.