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A hybrid intelligent model for acute hypotensive episode prediction with large-scale data
Information Sciences Pub Date : 2020-09-10 , DOI: 10.1016/j.ins.2020.08.033
Dazhi Jiang , Geng Tu , Donghui Jin , Kaichao Wu , Cheng Liu , Lin Zheng , Teng Zhou

Acute hypotensive episode (AHE) is a common serious postoperative complication in ICU, which may raise multiple system failure (especially of cardiac and respiratory kinds), and even cause death. Timely and effective clinical intervention is obviously vital to the saving of patients. AHE detection involves physiological time-series monitoring, processing and prediction technologies, which can offer insights to neuroscientists, biologists, and even provide support for clinicians. This paper presents a hybrid artificial intelligence model combined with CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise, a typical method for physiological signal decomposition), deep learning, multiple gene expression programming and fuzzy expert system for AHE detection. In this paper, the physiological signal is selected from a benchmark dataset, for example MIMIC-II (Multiparameter Intelligent Monitoring in Intensive Care II), which collects large scale real patients’ data for clinical research. In the hybrid model, a typical signal decomposition method is employed for AHE signal processing, and an autoencoder based deep neural network is established for feature extraction. Finally, a reliable and explainable classifier is presented by fusing gene expression programming and the fuzzy method. Experimental results based on real data set demonstrate that the proposed method outperforms state-of-the-art AHE detection methods by achieving the prediction accuracy of 88.14% in 2866 records.



中文翻译:

具有大规模数据的急性低血压发作预测的混合智能模型

急性降血压发作(AHE)是ICU中常见的严重术后并发症,可能引起多系统衰竭(尤其是心脏和呼吸系统衰竭),甚至导致死亡。及时有效的临床干预显然对挽救患者至关重要。AHE检测涉及生理时间序列监视,处理和预测技术,可以为神经科学家,生物学家提供见解,甚至为临床医生提供支持。本文提出了一种混合的人工智能模型,结合了CEEMDAN(具有自适应噪声的完整集合经验模式分解,一种生理信号分解的典型方法),深度学习,多基因表达编程和用于AHE检测的模糊专家系统。在本文中,生理信号是从基准数据集中选择的,例如MIMIC-II(重症监护中的多参数智能监控II),该数据会收集大量真实患者的数据以进行临床研究。在混合模型中,采用典型的信号分解方法进行AHE信号处理,并建立了基于自动编码器的深度神经网络进行特征提取。最后,通过融合基因表达程序和模糊方法,提出了一种可靠且可解释的分类器。基于真实数据集的实验结果表明,该方法通过在2866条记录中达到88.14%的预测准确率,胜过了最新的AHE检测方法。收集大量真实患者的数据用于临床研究。在混合模型中,采用典型的信号分解方法进行AHE信号处理,并建立了基于自动编码器的深度神经网络进行特征提取。最后,通过融合基因表达程序和模糊方法,提出了一种可靠且可解释的分类器。基于真实数据集的实验结果表明,该方法通过在2866条记录中达到88.14%的预测准确率,胜过了最新的AHE检测方法。收集大量真实患者的数据用于临床研究。在混合模型中,采用典型的信号分解方法进行AHE信号处理,并建立了基于自动编码器的深度神经网络进行特征提取。最后,通过融合基因表达程序和模糊方法,提出了一种可靠且可解释的分类器。基于真实数据集的实验结果表明,该方法通过在2866条记录中达到88.14%的预测准确率,胜过了最新的AHE检测方法。通过融合基因表达程序和模糊方法,提出了一种可靠且可解释的分类器。基于真实数据集的实验结果表明,该方法通过在2866条记录中达到88.14%的预测准确率,胜过了最新的AHE检测方法。通过融合基因表达程序和模糊方法,提出了一种可靠且可解释的分类器。基于真实数据集的实验结果表明,该方法通过在2866条记录中达到88.14%的预测准确率,胜过了最新的AHE检测方法。

更新日期:2020-09-10
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