A light-weight symptom checker and its methodological validation

Early recognition of diseases in pets is essential, yet owners often face challenges in interpreting clinical symptoms. Digital symptom checkers offer a promising approach to encode veterinary knowledge, but their reliability and diagnostic accuracy remain largely unvalidated. This study addresses this gap through a method validation of a expert-knowledge-based veterinary symptom checker using synthetically generated test cases, enabling systematic exploration of the symptom-disease space in the absence of clinical data.

System performance was quantified using simulated user-checker dialogs across [Formula: see text]550 diseases for dogs and cats, respectively. Robustness and efficiency were evaluated through three research questions: convergence probability, convergence speed, and structural factors influencing convergence.

The system achieved full convergence under ideal conditions (100%), with rapid convergence (mean rank of one after [Formula: see text]20 questions) and short response times (0.213-0.258 msec per disease). Under probabilistic user-answering strategies, performance decreased slightly but remained robust, with non-converging cases rare and correct diagnoses typically among top-ranked results (ranks 1-6 for dogs; 1-4 for cats). Structural analysis identified the number and uniqueness of symptoms as key predictors of diagnostic difficulty, with significant variation across anatomical regions.

Findings confirm the system's internal consistency, robustness, and computational efficiency, establishing a validated foundation for evidence-based veterinary diagnostic support. Future work will include clinical and user studies to confirm performance under authentic conditions and address current limitations of synthetic data.