Evidence combination for incremental decision-making processes

Abstract

The establishment of a medical diagnosis is an incremental process highly fraught with uncertainty. At each step of this painstaking process, it may be beneficial to be able to quantify the uncertainty linked to the diagnosis and steadily update the uncertainty estimation using available sources of information, for example user feedback, as they become available. Using the example of medical data in general and EEG data in particular, we show what types of evidence can affect discrete variables such as a medical diagnosis and build a simple and computationally efficient evidence combination model based on the Dempster-Shafer theory