Predicting line of therapy transition via similar patient augmentation

Analyzing large EHR databases to predict cancer progression and treatments has become a hot trend in recent years. An increasing number of modern deep learning models have been proposed to find the milestones of essential patient medical journey characteristics to predict their disease status and give healthcare professionals valuable insights. However, most of the existing methods are lack of consideration for the inter-relationship among different patients. We believe that more valuable information can be extracted, especially when patients with similar disease statuses visit the same doctors. Towards this end, a similar patient augmentation-based approach named SimPA is proposed to enhance the learning of patient representations and further predict lines of therapy transition. Our experiment results on a real-world multiple myeloma dataset show that our proposed approach outperforms state-of-the-art baseline approaches in terms of standard evaluation metrics for classification tasks.