Self-attention based recurrent convolutional neural network for disease prediction using healthcare data

作者：Mohd. Usama, Belal Ahmad, Wenjing Xiao, M. Shamim Hossain, G. Muhammad · 发表于：Comput. Methods Programs Biomed. · 年份：2019 · DOI：10.1016/j.cmpb.2019.105191 · 被引用次数：61 · 研究领域：Computer Science、Medicine

BACKGROUND AND OBJECTIVE 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. METHODS 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 meanin...