Using the Future to Sort Out the Present: Rankprop and Multitask Learning for Medical Risk Evaluation
A patient visits the doctor; the doctor reviews the patient's history, asks questions, makes basic measurements (blood pressure, ...), and prescribes tests or treatment. The prescribed course of action is based on an assessment of patient risk--patients at higher risk are given more and faster attention. It is also sequential--it is too expensive to immediately order all tests which might later be of value. This paper presents two methods that together improve the accuracy of backprop nets on a pneumonia risk assessment problem by 10-50%. Rankprop improves on backpropagation with sum of squares error in ranking patients by risk. Multitask learning takes advantage of future lab tests available in the training set, but not available in practice when predictions must be made. Both methods are broadly applicable.