Pre-trained models are beneficial in cross-disease prediction enhancement

What is it about?

Using innovative self-supervised learning techniques, we constructed a pre-trained model based on laboratory trajectories of patients with diabetes and hypertension. This model was successfully transferred to the prediction of outcomes in patients undergoing percutaneous coronary intervention (PCI), a population too small to train deep learning models independently. The approach improved prediction accuracy for target vessel revascularization (65% to 92%).

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Why is it important?

The method involves no manual labeling, effectively handles irregular and missing values, and relies on only six common laboratory tests, ensuring no additional clinical burden.