School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450000, China.
Cooperative Innovation Center of Internet Healthcare, Zhengzhou University, Zhengzhou 450000, China.
Math Biosci Eng. 2022 Jul 13;19(10):9966-9982. doi: 10.3934/mbe.2022465.
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.
中风仍然是中国最常见的死因。对于中风患者的死亡率预测具有重要意义，特别是在分析不可忽视因素之间的复杂相互作用方面。在本文中，我们提出了一种门控时空相关网络（GSTCNet）来预测一年后中风患者的死亡率。基于血管事件、慢性病、医疗使用和手术四类风险因素，我们设计了一种门控相关图卷积核来捕获空间特征，并增强特征类别之间的空间相关性。Bi-LSTM 表示五个时间戳的时间特征。然后，将新的门控相关注意力机制连接到 Bi-LSTM 上，以实现时空相关性的综合挖掘。我们使用从当地医院神经内科获得的 2275 名患者的数据构建了一系列连续实验。实验结果表明，所提出的模型在每个评估指标上都取得了有竞争力的结果，达到了 89.17%的 AUC、97.75%的精度、95.33%的召回率和 95.19%的 F1 分数。特征类别和时间戳的可解释性分析也验证了该模型对中风的潜在应用价值。
