Digitally enriching a high-risk population for pancreatic cancer using routine blood-based measures and clinical histories
A custom Transformer neural network was trained on patient medical histories, including diagnoses and blood tests over time, to predict pancreatic cancer risk years in advance. The model achieved good performance across settings by using specialized data encoding and imbalance handling techniques. This work establishes a foundation for a digital tool to screen high-risk populations.