Learning: The general ML workflow starts with partitioning the data into a training set (Train), a development set (Dev) and a test set (Test). The trajectories for training input are generated by sampling continuous subsequences of diagnoses for each patient’s diagnosis history, each starting with the first record but with different endpoints. The training and development sets are used for training so as to minimize the prediction error—that is, the difference between a risk score function (prediction) and a step function (observation), summed over all instances. Prediction: A model’s ability to accurately predict is evaluated using the withheld test set. The prediction model, depending on the prediction threshold selected from among possible operational points, discriminates between patients at higher and lower risk of pancreatic cancer. The risk model can guide the development of surveillance initiatives.
