Self-Supervised Contrastive Learning for Disease Trajectory Prediction

Early and accurate disease trajectory prediction is critical for personalized medicine and proactive healthcare management. In this study, we propose a self-supervised contrastive learning framework for modeling patient disease progression using structured electronic health records (EHRs). Unlike conventional supervised approaches, our method pretrains patient trajectory embeddings by contrasting similar and dissimilar patient histories, enabling effective representation learning with limited labeled data. We evaluated our model on a synthetic healthcare data set and compared it with eight state-of-the-art baselines, including LSTM, BiLSTM, GRU, CNN-LSTM, Transformer, SimCLR, and MoCo. The proposed model achieves an accuracy of 87.5%, an F1-score of 85.6%, and an AUC-ROC of 90.1%, surpassing the best baseline by 1.4% in AUC-ROC. Ablation studies highlight the importance of contrastive learning, with a performance drop of 5.6% in AUC-ROC when it is removed. Additionally, our model demonstrates strong generalization across different disease types, achieving 87.0% accuracy for cardiovascular diseases and 85.9% for diabetes. Computational analysis shows that our method reduces inference time to 7.6 ms, making it a practical solution for real-time clinical applications. These findings suggest that self-supervised contrastive learning is a promising approach for enhancing disease trajectory prediction, with potential applications in early diagnosis, risk stratification, and personalized treatment planning.