Abstract: Disease diagnosis prediction aims to use electronic health data to model disease progression patterns and predict the future health status of patients,and is widely used in assisting clinical decision-making,healthcare services and other fields.In order to further explore the valuable information in the medical records,a disease diagnosis prediction algorithm based on contrastive learning is proposed.Contrastive learning provides self-supervised training signals for the model by measuring the similarity between samples,which can improve the information capture ability of the model.The proposed algorithm excavates the common knowledge between similar patients through contrastive training,and enhances the ability of the model to learn patient representations.In order to capture more comprehensive common information,the information of similar groups of the target patient is further explored as auxiliary information to characterize the health status of the target patient.Experimental results on the public dataset show that compared with the Retain,Dipole,LSAN and GRASP algorithms,the proposed algorithm improves AUROC and AUPRC of the readmission prediction task by more than 2.9% and 8.1% respectively,and Recall@10 and MAP@10 of the diagnosis prediction task by 2.1% and 1.8%,respectively.

Key words: Diagnosis prediction, Deep learning, Contrastive learning, Clustering, Similar patients