Recurrent probabilistic neural network-based short-term prediction for acute hypotension and ventricular fibrillation
Recurrent probabilistic neural network-based short-term prediction for acute hypotension and ventricular fibrillation
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DOI：
10.1038/s41598-020-68627-6
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发表时间：
2020-07
影响因子：
4.6
通讯作者：
T. Tsuji;Tomonori Nobukawa;Akihisa Mito;H. Hirano;Z. Soh;R. Inokuchi;E. Fujita;Y. Ogura;S. Kaneko;R. Nakamura;N. Saeki;M. Kawamoto;M. Yoshizumi
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作者：
T. Tsuji;Tomonori Nobukawa;Akihisa Mito;H. Hirano;Z. Soh;R. Inokuchi;E. Fujita;Y. Ogura;S. Kaneko;R. Nakamura;N. Saeki;M. Kawamoto;M. Yoshizumi
在本文中，我们提出了一种利用生物信号（例如心率、RR 间期和血压）来预测由低血压、心室颤动和未诊断的多种疾病引发的急性临床恶化的新方法。试图预测此类急性临床恶化事件的努力最近受到了研究人员的广泛关注，但其中大多数都是针对单一症状。该方法的显着特点是通过应用嵌入隐马尔可夫模型和高斯混合模型的循环概率神经网络将事件的发生表现为概率。此外，它的机器学习方案允许它从样本数据中学习并将其应用于广泛的症状。使用 Physionet 和东京大学医院提供的数据集测试了所提出方法的性能。结果表明，该方法对急性低血压患者的预测准确率为92.5%，并且可以在心室颤动发生前5 min预测其发生，准确率为82.5%。此外，可以在多种疾病发生前7分钟进行预测，准确率超过90%。
In this paper, we propose a novel method for predicting acute clinical deterioration triggered by hypotension, ventricular fibrillation, and an undiagnosed multiple disease condition using biological signals, such as heart rate, RR interval, and blood pressure. Efforts trying to predict such acute clinical deterioration events have received much attention from researchers lately, but most of them are targeted to a single symptom. The distinctive feature of the proposed method is that the occurrence of the event is manifested as a probability by applying a recurrent probabilistic neural network, which is embedded with a hidden Markov model and a Gaussian mixture model. Additionally, its machine learning scheme allows it to learn from the sample data and apply it to a wide range of symptoms. The performance of the proposed method was tested using a dataset provided by Physionet and the University of Tokyo Hospital. The results show that the proposed method has a prediction accuracy of 92.5% for patients with acute hypotension and can predict the occurrence of ventricular fibrillation 5 min before it occurs with an accuracy of 82.5%. In addition, a multiple disease condition can be predicted 7 min before they occur, with an accuracy of over 90%.