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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Data-driven Discovery of a Sepsis Patients Severity Prediction in the ICU via Pre-training BiLSTM Networks
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Data-driven Discovery of a Sepsis Patients Severity Prediction in the ICU via Pre-training BiLSTM Networks

机译：通过预训练BiLSTM网络在ICU中进行数据驱动的脓毒症患者严重程度预测的发现

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Sepsis is the third-highest mortality disease in intensive care units(ICU) and expensive treatment costs, but the best treatment strategy remains uncertain. In this paper, we proposed a pre-training bidirectional LSTM Networks to predict the Sepsis severity of patients in ICU. Most previous models for severity prediction rely on the multi-task recurrent neural networks. In addition, state-of-the-art neural models based on attention mechanisms do not fully utilize information of organ systems that may be the most crucial features for severity prediction. To address these issues, we propose an end-to-end recurrent neural model which incorporates simultaneously analyses different organ systems and intuitively reflect the condition of the patients in a timely fashion. Specifically, we apply a pre-training technique in our model to combines it with labeled data via multi-task learning. Experimental results on the real-world clinical dataset (MIMIC-III), one of the most popular sepsis severity prediction tasks, demonstrate that our model outperforms existing state-of-the-art models.
机译：脓毒症是重症监护病房（ICU）的第三高死亡率疾病，治疗费用昂贵，但最佳治疗策略仍不确定。在本文中，我们提出了一种预训练的双向LSTM网络，以预测ICU患者的败血症严重程度。用于严重性预测的大多数先前模型都依赖于多任务递归神经网络。此外，基于注意力机制的最新神经模型并未充分利用器官系统的信息，而器官系统的信息可能是严重程度预测的最关键特征。为了解决这些问题，我们提出了一种端到端的递归神经模型，该模型可以同时分析不同的器官系统并及时直观地反映患者的状况。具体而言，我们在模型中应用了预训练技术，以通过多任务学习将其与标记数据结合在一起。现实世界中临床数据集（MIMIC-III）（最流行的败血症严重程度预测任务之一）的实验结果表明，我们的模型优于现有的最新模型。

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