Zhang Shuo, Wang Jing, Pei Lulu, Liu Kai, Gao Yuan, Fang Hui, Zhang Rui, Zhao Lu, Sun Shilei, Wu Jun, Song Bo, Dai Honghua, Li Runzhi, Xu Yuming
IEEE J Biomed Health Inform. 2022 Apr;26(4):1903-1910. doi: 10.1109/JBHI.2021.3123657. Epub 2022 Apr 14.
Clinically, physicians collect the benchmark medical data to establish archives for a stroke patient and then add the follow up data regularly. It has great significance on prognosis prediction for stroke patients. In this paper, we present an interpretable deep learning model to predict the one-year mortality risk on stroke. We design sub-modules to reconstruct features from original clinical data that highlight the dissimilarity and temporality of different variables. The model consists of Bidirectional Long Short-Term Memory (Bi-LSTM), in which a novel correlation attention module is proposed that takes the correlation of variables into consideration. In experiments, datasets are collected clinically from the department of neurology in a local AAA hospital. It consists of 2,275 stroke patients hospitalized in the department of neurology from 2014 to 2016. Our model achieves a precision of 0.9414, a recall of 0.9502 and an F1-score of 0.9415. In addition, we provide the analysis of the interpretability by visualizations with reference to clinical professional guidelines.
临床上，医生会收集中风患者的基准医学数据以建立档案，然后定期添加随访数据。这对中风患者的预后预测具有重要意义。在本文中，我们提出了一种可解释的深度学习模型来预测中风患者的一年死亡风险。我们设计子模块从原始临床数据中重建特征，突出不同变量的差异和时间性。该模型由双向长短期记忆网络（Bi-LSTM）组成，其中提出了一种新颖的相关注意力模块，该模块考虑了变量之间的相关性。在实验中，临床数据集是从当地一家三级甲等医院的神经内科收集的。它由2014年至2016年在该神经内科住院的2275名中风患者组成。我们的模型实现了0.9414的精确率、0.9502的召回率和0.9415的F1分数。此外，我们参照临床专业指南通过可视化提供可解释性分析。
