Subsumptive reflection in SNOMED CT: a large description logic-based terminology for diagnosis

TL;DR

This paper introduces a simplified model for diagnostic inference in SNOMED CT, a complex biomedical terminology, by classifying clinical features and analyzing their logical implications using a large database and corpus.

Contribution

It proposes a novel classification of clinical features and examines their impact on inference in SNOMED CT, providing insights for improving diagnostic processes.

Findings

10% of finding-disorder links are concomitant in both assertion and negation

90% are either assertion or negation

70% of links do not share any common system

Abstract

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.