Subsumptive reflection in SNOMED CT: a large description logic-based
  terminology for diagnosis
  Description logic (DL) based biomedical terminology (SNOMED CT) is used
routinely in medical practice. However, diagnostic inference using such
terminology is precluded by its complexity. Here we propose a model that
simplifies these inferential components. We propose three concepts that
classify clinical features and examined their effect on inference using SNOMED
CT. We used PAIRS (Physician Assistant Artificial Intelligence Reference
System) database (1964 findings for 485 disorders, 18 397 disease feature
links) for our analysis. We also use a 50-million medical word corpus for
estimating the vectors of disease-feature links. Our major results are 10% of
finding-disorder links are concomitant in both assertion and negation where as
90% are either concomitant in assertion or negation. Logical implications of
PAIRS data on SNOMED CT include 70% of the links do not share any common system
while 18% share organ and 12% share both system and organ. Applications of
these principles for inference are discussed and suggestions are made for
deriving a diagnostic process using SNOMED CT. Limitations of these processes
and suggestions for improvements are also discussed.
