Subsumptive reflection in SNOMED CT: a large description logic-based terminology for diagnosis
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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 finding-disorder links are concomitant in both assertion and negation where as 90 PAIRS data on SNOMED CT include 70 while 18 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.
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