FIGURE 4.

Top performing model. A, Overview of Deep MTLR. The model combines EMR features with tumor volume using a neural network and learns to jointly predict the probability of death at all intervals on the discretized time axis, allowing it to achieve good performance in both the binarized and lifetime risk prediction tasks. A predicted survival curve can be constructed for each individual to determine the survival probability at any timepoint. B, Importance of combined input data for performance on the binary endpoint. Training the deep MTLR on EMR features only led to notably worse performance. Furthermore, using a deep convolutional neural network in place of tumor volume did not improve the 2-year AUROC.