Using Description Logic and Abox Abduction to Capture Medical Diagnosis

Abstract

Medical diagnosis can be defined as the detection of a disease by examining a patient’s signs, symptoms and history. Diagnostic reasoning can be viewed as a process of testing hypotheses guided by symptoms and signs. Solutions to diagnostic problems can be found by generating a limited number of hypotheses early in the diagnostic process and using them to guide subsequent collection of data. Each hypothesis, if correct, can be used to pre-dict what additional findings must be present, and the diagnostic process would then be a guided search for these findings. The process depends on the medical knowledge available. Description Logic-based ontologies provide class definitions (i.e., the necessary and sufficient conditions for defining class membership). In medicine, these definitions correspond to diagnostic criteria, i.e., the particular form of diseases should be associated with the relevant disease categories. In this paper, we model medical diagnosis as an (iterative) abductive reasoning process using ALC. ALC is employed to take advantage of its inference services. However, the inference capabilities provided by DL are not sufficient for diagnosis purposes. The contributions of the paper include: (1) arguing for the need for a disease-symptoms ontology, (2) proposing an ontological representation which, beside facilitating abductive reasoning, takes into account the diagnostic criteria such that specific patient conditions can be classified under a specific disease, and (3) employing Abox abduction to capture the process of medical diagnosis (the process of generating and testing hypotheses) on this proposed representation.