Background

The timeliness of detection of a sepsis incidence in progress is a crucial factor in the outcome for the patient. Machine learning models built from data in electronic health records can be used as an effective tool for improving this timeliness, but so far, the potential for clinical implementations has been largely limited to studies in intensive care units. This study will employ a richer data set that will expand the applicability of these models beyond intensive care units. Furthermore, we will circumvent several important limitations that have been found in the literature: (1) Model evaluations neglect the clinical consequences of a decision to start, or not start, an intervention for sepsis. (2) Models are evaluated shortly before sepsis onset without considering interventions already initiated. (3) Machine learning models are built on a restricted set of clinical parameters, which are not necessarily measured in all departments. (4) Model performance is limited by current knowledge of sepsis, as feature interactions and time dependencies are hard-coded into the model.

Methods

In this study, we present a model to overcome these shortcomings using a deep learning approach on a diverse multicenter data set. We used retrospective data from multiple Danish hospitals over a seven-year period. Our sepsis detection system is constructed as a combination of a convolutional neural network and a long short-term memory network. We assess model quality by standard concepts of accuracy as well as clinical usefulness, and we suggest a retrospective assessment of interventions by looking at intravenous antibiotics and blood cultures preceding the prediction time.

Results

Results show performance ranging from AUROC 0.856 (3 h before sepsis onset) to AUROC 0.756 (24 h before sepsis onset). Evaluating the clinical utility of the model, we find that a large proportion of septic patients did not receive antibiotic treatment or blood culture at the time of the sepsis prediction, and the model could, therefore, facilitate such interventions at an earlier point in time.

Conclusion

We present a deep learning system for early detection of sepsis that can learn characteristics of the key factors and interactions from the raw event sequence data itself, without relying on a labor-intensive feature extraction work. Our system outperforms baseline models, such as gradient boosting, which rely on specific data elements and therefore suffer from many missing values in our dataset.

中文翻译：

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利用对电子健康记录事件序列的深度学习来早期检测败血症。

背景

检测正在进行的败血症发病率的及时性是患者结果的关键因素。根据电子健康记录中的数据构建的机器学习模型可以用作提高这种及时性的有效工具，但到目前为止，临床实施的潜力在很大程度上仅限于重症监护病房的研究。这项研究将使用更丰富的数据集，将这些模型的适用性扩展到重症监护病房之外。此外，我们将规避文献中发现的几个重要限制：（1）模型评估忽略了决定开始或不开始脓毒症干预的临床后果。(2) 在脓毒症发作前不久评估模型，而不考虑已经启动的干预措施。(3) 机器学习模型建立在一组有限的临床参数上，不一定在所有部门都进行测量。(4) 模型性能受到脓毒症当前知识的限制，因为特征交互和时间依赖性被硬编码到模型中。

方法

在这项研究中，我们提出了一个模型，在多样化的多中心数据集上使用深度学习方法来克服这些缺点。我们使用了来自丹麦多家医院七年时间的回顾性数据。我们的脓毒症检测系统由卷积神经网络和长短期记忆网络的组合构成。我们通过准确性和临床实用性的标准概念来评估模型质量，我们建议通过在预测时间之前查看静脉注射抗生素和血培养对干预措施进行回顾性评估。

结果

结果显示的性能范围从 AUROC 0.856（脓毒症发作前 3 小时）到 AUROC 0.756（脓毒症发作前 24 小时）。评估模型的临床效用，我们发现很大比例的脓毒症患者在脓毒症预测时没有接受抗生素治疗或血培养，因此该模型可以在更早的时间点促进此类干预。

结论

我们提出了一种用于早期检测败血症的深度学习系统，该系统可以从原始事件序列数据本身中学习关键因素和相互作用的特征，而无需依赖劳动密集型的特征提取工作。我们的系统优于基线模型，例如梯度提升，这些模型依赖于特定的数据元素，因此在我们的数据集中存在许多缺失值。