A Convolutional Neural Network Model for Online Medical Guidance

• Main Authors: Cuili Yao, Yue Qu, Bo Jin, Li Guo, Chao Li, Wenjuan Cui, Lin Feng
• Format: Article
• Language: English
• Published: IEEE 2016-01-01
• Series: IEEE Access
• Subjects: Medical guidance; convolutional neural network; name entity recognition
• Online Access: https://ieeexplore.ieee.org/document/7523892/

The aging population of China is becoming increasingly more prominent, thus increasing the burden on medical resources. Therefore, the use of data mining technology to improve the efficiency of disease diagnosis has the following important significance. For hospitals, such technology can reduce the cost of providing one-on-one guidance to patients and the probability of registration errors. For patients, it can save time and energy spent on hospital visits; in addition, through remote access, patients can follow the automated guidance at home to complete registration, thereby enhancing admission efficiency. For internet users, such technology enables self-checking of these users' health conditions on a regular basis; based on certain main symptoms, possible diseases can be pre-diagnosed, thus providing a risk warning. Online medical guidance has become a very important step. To this end, we focus on employing the data mining technology to enhance the performance of online medical guidance. In this paper, we propose a medical diagnosis method called the named entity recognition method and a convolutional neural network model. We apply our proposed method and model as an innovative framework for hospitalization guidance to provide human-like, comprehensive and informative automated medical consultations. We perform experiments on real-world datasets. The experimental results show that our methods achieve state-of-the-art performance compared with baselines.