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首页> 外文期刊>Journal of visual communication & image representation >Real-time sepsis severity prediction on knowledge graph deep learning networks for the intensive care unit
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Real-time sepsis severity prediction on knowledge graph deep learning networks for the intensive care unit

机译：实时脓毒症严重性预测知识图深入学习网络的重症监护室

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摘要

Sepsis is the third-highest mortality disease in intensive care units (ICUs). In this paper, we proposed a deep learning model for predicting the severity of sepsis patients. Most existing models based on attention mechanisms do not fully utilize knowledge graph based information for different organ systems, such that might constitute crucial features for predicting the severity of sepsis patients. Therefore, we have employed a medical knowledge graph as a reliable and robust source of side information. End-to-end neural networks that incorporate analyses of various organ systems simultaneously and intuitively were developed in the proposed model to reflect upon the condition of patients in a timely fashion. We have developed a pre-training technique in the proposed model to combine it with labeled data by multi-task learning. Experimental results on realworld clinical datasets, MIMIC-III and eIR, demonstrate that our model outperforms state-of-the-art models in predicting the severity of sepsis patients.
机译：脓毒症是强化护理单位（ICU）的第三高死亡率疾病。在本文中，我们提出了预测败血症患者严重程度的深度学习模型。基于注意机制的大多数现有模型并未充分利用基于知识图的基于不同器官系统的信息，这可能构成预测败血症患者的严重程度的关键特征。因此，我们使用了医学知识图形作为可靠且坚固的侧面信息来源。在所提出的模型中开发了同时和直观地纳入各种器官系统分析的端到端神经网络，以及时地反映患者的状况。我们在所提出的模型中开发了一种预训练技术，以通过多任务学习将其与标记数据相结合。 Realworld临床数据集，模拟-III和EIR的实验结果表明，我们的模型优于最先进的模型来预测败血症患者的严重程度。

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