A probabilistic algorithm with user feedback loop for decision making during the hospital triage process

Abstract

In this paper, we describe a probabilistic algorithm with user feedback loop, which can be used for decision making during the patient triage process. Given an R{x, y} the method relies on the user defining a set of x values (i.e. symptoms) and the algorithm returns a collection of y values as a hidden layer (possible diseases), taking into consideration a possible false negative user reporting, by looking into candidate values of y and identifying x values (symptoms) which have not been initially provided by the user. The user can specify parameters such as the minimum probability ratio of the final output, the minimum probability ratio of the y values for which the non-user given x values will be reevaluated, and the maximum number of user feedback loops. In order to validate the method, we use a comprehensive 2012 Medicare Claims dataset with 15 million cases.