Title: Learning a Cost-effective Strategy on Incomplete Medical Data
Authors: Mengxiao Zhu and Haogang Zhu
Abstract: Deep learning techniques have shown remarkable success in classification tasks. However, the prerequisite for high accuracy is that data is easily accessible, which is unrealistic since most features come at a cost. Under a medical scenario, each feature is associated with a medical test that costs a certain amount of money. And doctors would ask patients to do consecutive tests until they are confident enough to make a final diagnosis, whereas the overall cost incurred during the process is often ignored. In this paper, we propose to learn a cost-effective strategy which at the same time hastens the decision process. As is often the case where both medical records and initial feature values of a new patient are incomplete, we design a framework consisting of two components, the oracle classifier and the feature selector, to tackle the challenges. The classifier incorporates a sequence encoder that can handle any set of feature values in various sizes. And the selector efficiently learns the cost-effective strategy based on the state-of-art reinforcement learning techniques. Experimental results have shown that under the same classification accuracy, our strategy is superior to other related approaches in terms of the overall cost.
