14. A method for knowledge-based interpretable predictive modeling of patients, the method comprising: training, with machine training using training data for a plurality of previous lung cancer patients, a graphic model to predict survivability of lung cancer based on relationships between variables from a lung cancer expert, the training data including previous patient values for the variables, the variables including the survivability; applying, with a processor, current patient values of the variables for a current lung cancer patient to the graphic model, the graphic model configured to predict even with one of the variables not having a current patient value as a function of the relationships; displaying a representation of the graphic model, the representation showing the variables and the relationships remaining after training; and displaying the survivability for the current lung cancer patient predicted by the graphic model, wherein the variables comprise tumor load, T-stage, N-stage, number of lymph node stations, WHO performance, and the survivability.
18. The method of claim 14 wherein the graphic model configured to predict even with one of the variables not having a current patient value comprises using a substitute value for the one of the variables, the substitute value selected from a distribution learned from the training data.