GSTCNet: Gated spatio-temporal correlation network for stroke mortality prediction

Shuo Zhang; Yonghao Ren; Jing Wang; Bo Song; Runzhi Li; Yuming Xu; Shuo Zhang; Yonghao Ren; Jing Wang; Bo Song; Runzhi Li; Yuming Xu

doi:10.3934/mbe.2022465

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 10: 9966-9982. doi: 10.3934/mbe.2022465

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GSTCNet: Gated spatio-temporal correlation network for stroke mortality prediction

1.
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China
2.
Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China
3.
Department of Neurology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450000, China
4.
NHC Key Laboratory of Prevention and Treatment of Cerebrovascular Diseases, Zhengzhou 450000, China

Academic Editor: Buzhou Tang

Received: 04 May 2022 Revised: 27 June 2022 Accepted: 03 July 2022 Published: 13 July 2022

Stroke continues to be the most common cause of death in China. It has great significance for mortality prediction for stroke patients, especially in terms of analyzing the complex interactions between non-negligible factors. In this paper, we present a gated spatio-temporal correlation network (GSTCNet) to predict the one-year post-stroke mortality. Based on the four categories of risk factors: vascular event, chronic disease, medical usage and surgery, we designed a gated correlation graph convolution kernel to capture spatial features and enhance the spatial correlation between feature categories. Bi-LSTM represents the temporal features of five timestamps. The novel gated correlation attention mechanism is then connected to the Bi-LSTM to realize the comprehensive mining of spatio-temporal correlations. Using the data on 2275 patients obtained from the neurology department of a local hospital, we constructed a series of sequential experiments. The experimental results show that the proposed model achieves competitive results on each evaluation metric, reaching an AUC of 89.17%, a precision of 97.75%, a recall of 95.33% and an F1-score of 95.19%. The interpretability analysis of the feature categories and timestamps also verified the potential application value of the model for stroke.
- stroke,
- gate mechanism,
- correlation,
- Bi-LSTM,
- graph convolutional network
Citation: Shuo Zhang, Yonghao Ren, Jing Wang, Bo Song, Runzhi Li, Yuming Xu. GSTCNet: Gated spatio-temporal correlation network for stroke mortality prediction[J]. Mathematical Biosciences and Engineering, 2022, 19(10): 9966-9982. doi: 10.3934/mbe.2022465

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Abstract

Stroke continues to be the most common cause of death in China. It has great significance for mortality prediction for stroke patients, especially in terms of analyzing the complex interactions between non-negligible factors. In this paper, we present a gated spatio-temporal correlation network (GSTCNet) to predict the one-year post-stroke mortality. Based on the four categories of risk factors: vascular event, chronic disease, medical usage and surgery, we designed a gated correlation graph convolution kernel to capture spatial features and enhance the spatial correlation between feature categories. Bi-LSTM represents the temporal features of five timestamps. The novel gated correlation attention mechanism is then connected to the Bi-LSTM to realize the comprehensive mining of spatio-temporal correlations. Using the data on 2275 patients obtained from the neurology department of a local hospital, we constructed a series of sequential experiments. The experimental results show that the proposed model achieves competitive results on each evaluation metric, reaching an AUC of 89.17%, a precision of 97.75%, a recall of 95.33% and an F1-score of 95.19%. The interpretability analysis of the feature categories and timestamps also verified the potential application value of the model for stroke.

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