# Model Establishment of Cross-Disease Course Prediction Using Transfer Learning
## 2 Related Works  
In previous studies, transfer learning has been mostly used in image analysis and it is no exception in the medical field, with various studies [18; 19; 20; 21] all achieving significant improvements in the accuracy of medical image recognition applications. In the application of EHRs data, there is also parameter transfer of the same prediction task model using cross-hospital data [25]. [PERSON] et al. [26] used Artificial Neural Networks (ANNs) as a model basis for transfer learning and applied it to train de-identified models on small EHRs datasets. [PERSON] et al. [27] combined transfer learning with decision tree, and, under the sharing of pre-training model parameters, helped hospitals with a small amount of data to improve the accuracy of the mortality prediction model. [PERSON] et al. [28] extracted features from physiological signal data using time series feature extraction models based on Recurrent Neural Network (RNN)--TimeNet [29] and HealthNet [28]--and then transferred the parameters of the pretrained model to the target model for binary mortality prediction. Medical information is roughly divided into medical images and medical data [18]. Medical images include ultrasound images, computed tomography scans, and MRI scans [19]. In a complete medical system, a huge number of medical images are accumulated, but there is a lack of labeling and diagnosis of images. As labeling requires a lot of time and manpower, its accuracy is reduced due to long-term operations. Therefore, it is somewhat challenging to use medical images in the development of deep learning applications. Medical data comprise body measurement data, diagnosis, and medication in the patient's medical records. There are also limitations in the application of these medical data. Among these, doctors in different hospitals have different practices in compiling medical treatment records; it is hard to notice certain diseases during specific medical task predictions because of the difficulty in detecting the symptoms, which result in patients being unable to seek medical treatment within a certain period of time; a lack of data for a complete training process; and poor performance of the prediction model. The methods of applying shared parameters to the target task prediction model in transfer learning can be divided into freezing and fine-tuning [10]. Figure 1 is a schematic diagram of the training process of the two methods. Freezing is to transfer the trained parameters of the source task prediction model to the first n-layers of the target task prediction model, then freeze the first n-layers without parameter training, initialize the rest of the target task prediction model, and train for the target task to achieve the target prediction outcome. Fine-tuning is to backpropagate the error during training of the target task prediction model to the n-layers copied from the source task prediction model to update the weights, so that the copied parameters are adjusted to match the prediction of the target task. Choosing one of the two methods depends on the size of the target task dataset and the number of parameters migrated from the source task prediction model. If the target dataset is small, and the source task prediction model has a large number of parameters pre-trained, freezing is usually chosen to avoid model overfitting. If the target task dataset is large or the source task prediction model has a small amount of pre-trained parameters, the basic parameters can be fine-tuned to match the prediction of the target task and improve performance. Although many deep learning mechanisms have been proposed to deal with the problem of model building for a single disease prediction, all of them rely on a huge amount of training data [20; 21]. Unfortunately, some diseases are not as common so data regarding these diseases cannot be retrieved from EHRs.