School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
Department of Software Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Comput Methods Programs Biomed. 2020 Jul;190:105191. doi: 10.1016/j.cmpb.2019.105191. Epub 2019 Nov 11.
Nowadays computer-aided disease diagnosis from medical data through deep learning methods has become a wide area of research. Existing works of analyzing clinical text data in the medical domain, which substantiate useful information related to patients with disease in large quantity, benefits early-stage disease diagnosis. However, benefits of analysis not achieved well when the traditional rule-based and classical machine learning methods used; which are unable to handle the unstructured clinical text and only a single method is not able to handle all challenges related to the analysis of the unstructured text, Moreover, the contribution of all words in clinical text is not the same in the prediction of disease. Therefore, there is a need to develop a neural model which solve the above clinical application problems, is an interesting topic which needs to be explored.
Thus considering the above problems, first, this paper present self-attention based recurrent convolutional neural network (RCNN) model using real-life clinical text data collected from a hospital in Wuhan, China. This model automatically learns high-level semantic features from clinical text by using bi-direction recurrent connection within convolution. Second, to deal with other clinical text challenges, we combine the ability of RCNN with the self-attention mechanism. Thus, self-attention gets the focus of the model on essential convolve features which have effective meaning in the clinical text by calculating the probability of each convolve feature through softmax.
The proposed model is evaluated on real-life hospital dataset and used measurement metrics as Accuracy and recall. Experiment results exhibit that the proposed model reaches up to accuracy 95.71%, which is better than many existing methods for cerebral infarction disease.
This article presented the self-attention based RCNN model by combining the RCNN with self-attention mechanism for prediction of cerebral infarction disease. The obtained results show that the presented model better predict the cerebral infarction disease risk compared to many existing methods. The same model can also be used for the prediction of other disease risks.
如今，通过深度学习方法从医学数据中辅助疾病诊断已成为一个广泛的研究领域。现有的分析医学领域临床文本数据的工作从大量与疾病相关的患者有用信息中受益，有利于疾病的早期诊断。然而，当使用传统的基于规则和经典的机器学习方法时，这些分析工作并不能很好地发挥作用；这些方法无法处理非结构化的临床文本，而且单一的方法也无法处理与非结构化文本分析相关的所有挑战。此外，临床文本中所有单词在疾病预测中的贡献并不相同。因此，需要开发一种能够解决上述临床应用问题的神经模型，这是一个需要探索的有趣话题。
因此，考虑到上述问题，本文首先提出了一种基于自注意力的递归卷积神经网络（RCNN）模型，该模型使用从中国武汉一家医院收集的真实临床文本数据。该模型通过卷积内的双向递归连接，自动从临床文本中学习高级语义特征。其次，为了处理其他临床文本挑战，我们将 RCNN 的能力与自注意力机制相结合。因此，自注意力机制通过使用 softmax 计算每个卷积特征的概率，使模型的注意力集中在临床文本中具有有效意义的重要卷积特征上。
该模型在真实的医院数据集上进行了评估，并使用准确度和召回率作为测量指标。实验结果表明，所提出的模型达到了高达 95.71%的准确度，优于许多现有的脑梗死疾病预测方法。
本文通过将 RCNN 与自注意力机制相结合，提出了一种基于自注意力的 RCNN 模型，用于预测脑梗死疾病。所获得的结果表明，与许多现有的方法相比，所提出的模型能够更好地预测脑梗死疾病的风险。同样的模型也可以用于预测其他疾病的风险。
