# Model Establishment of Cross-Disease Course Prediction Using Transfer Learning
## 1 Introduction
From the literature review of the aforementioned medical task predictions, it is evident that most studies have applied deep learning to the prediction task of a single disease, but few studies have applied deep learning to the analysis and prediction of cross-diseases. Transfer learning [10] can be used for data with different but similar distributions, and its features can be used for different tasks to predict the transfer of parameters between models [10]. The feasibility and effects of transfer learning in deep learning can be understood through the research of [PERSON] et al. [11], and the characteristics of this method are exactly in line with the characteristics of medical information. Therefore, the concept of transfer learning can be applied to medical information, so that even medical tasks with poor predictive performance due to the lack of data can train predictive models with a minimum of effectively labeled data. Although transfer learning has been addressed in many studies [18; 19; 20; 21], these works mostly focus on image analysis. Unlike image analysis, transfer learning cannot be casually performed on EHRs data for multiple disease course prediction. The reason is that only few diseases share similar courses. Therefore, performing transfer learning on EHRs data should consider whether there is sufficient medical evidence that reveals a correlation be tween source (disease) domain and target (disease) domain. For example, in related studies on chronic diseases, the treatment of periodontal disease has been found to effectively alleviate the symptoms of RA [22,23]. Thus, it can be seen that there is a similar distribution of pathological characteristics between these two diseases. Therefore, we can use transfer learning (TL) in deep learning to analyze the data on periodontal disease (PD) and RA in EHRs, extract features through the time series model, and establish a predictive model for the development of a source disease based on PD medical records and transfer the predictive model for the development of the target disease based on RA medical records. Furthermore, many prediction and classification models involve the notion of a self-attention mechanism to improve the accuracy of a prediction result. However, such a self-attention mechanism usually cannot work well while we transfer a timeseries prediction model from a source domain with a high sampling rate to a target domain with a low sampling rate. As health status is presented by the patient's medical records, the parameters of the timeseries prediction model trained by the source disease course were transferred to the target prediction model for predicting the target disease course. For example, the target prediction model for prediction will be transferred from one built based on PD, in EHRs realized based on the RA visit record data, but the self-attention mechanism must be fine-tuned with a small dataset of the target disease to accurately predict its course. As a result, a new breed transfer learning in deep learning is desired to build a disease course prediction model that can transfer the model train with a self-attention mechanism from the source disease domain to the target disease domain. To do so, the main task of this study is to use the transfer learning framework with a self-attention mechanism to build a cross-disease course development prediction model and summarize the disease course development model of the existing chronic disease patient data. The purpose is to assist doctors in estimating the course of target diseases and recommend accurate treatment plans while the training data is not sufficient for building a deep learning model. In view of the lack of training data due to the characteristics of the chronic disease course, this study provides a solution to extract the development characteristics of each PD patient and predict the course development by applying deep learning models to the National Health Insurance Research Database in Taiwan [24].