Abstract
In order to mine information from medical health data and develop intelligent application-related issues, the multi-modal medical health data feature representation learning related content was studied, and several feature learning models were proposed for disease risk assessment. In the aspect of medical text feature learning, a medical text feature learning model based on convolutional neural network is proposed. The convolutional neural network text analysis technology is applied to the disease risk assessment application. The medical data feature representation adopts the deep learning method. The learning and extraction of different disease characteristics use the same method to realize the versatility of the model. A simple preprocessing of the experimental data samples, including its power frequency denoising and lead convolution regularization, constructs a convolutional neural network for medical data feature advancement and intelligent recognition. On the basis of it, several sets of experiments were carried out to discuss the influence of the convolution kernel and the choice of learning rate on the experimental results. In addition, comparative experiments with support vector machine, BP neural network and RBF neural network are carried out. The results show that the convolutional neural network used in this paper shows obvious advantages in recognition rate and training speed compared with other methods. In the aspect of time series data feature learning, a multi-channel convolutional self-encoding neural network is proposed. Analyze the connection between fatigue and emotional abnormalities and define the concept of emotional fatigue. The proposed multi-channel convolutional neural network is used to learn the data features, and the convolutional self-encoding neural network is used to learn the facial image data features. These two characteristics and the collected physiological data are combined to perform emotional fatigue detection. An emotional fatigue detection demonstration platform for multi-modal data feature fusion is established to realize data acquisition, emotional fatigue detection and emotional feedback. The experimental results verify the validity, versatility and stability of the model.
Highlights
Medical health data is a multi-modal, complex data that continues to grow rapidly
A medical text feature learning model based on convolutional neural network and intelligent recognition were proposed
The text analysis technique combined with word vector and convolutional neural network was applied to unstructured medical text feature extraction for disease risk assessment
Summary
Medical health data is a multi-modal, complex data that continues to grow rapidly. It contains a wealth of information. Calculate the error between the predicted value of the sample and the true target value of the sample, and perform layer-by-layer propagation from the output layer to the input layer to optimize the connection parameters between the layers in the structure, in order to minimize the cost function value This process is backward widely used convolutional neural networks [7]–[9], using supervised learning methods to learn the characteristic representation of raw input data. After the input medical data is convolved and pooled, the feature map is getting smaller and smaller, but after the hidden layer is propagated, the number of feature maps is gradually increasing This means that the number of extracted features is increasing, and the ECG signal information can be more fully represented.
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