Using probabilistic logic and the principle of maximum entropy for the analysis of clinical brain tumor data

Abstract

Dealing with uncertainty that is inherently present in any medical domain, is one of the major challenges when designing a medical decision support system. We demonstrate how probabilistic logic can be used to design medical knowledge bases at the example of analysing clinical brain tumor data. We use MECoRe, a system implementing probabilistic conditional logic, to create a knowledge base BT that contains medical knowledge originating from both statistical data as well as from medical experts. Any incomplete or unspecified knowledge is completed by MECoRe in an information-theoretically optimal way by employing the principle of maximum entropy. BT is evaluated with respect to a series of queries regarding diagnosis and prognosis, using a real documented patient case.