Hybrid Rule-Based and Machine Learning Classification of Military Working Dog Medical Problems Using SNOMED CT and the Veterinary Extension
This paper presents a hybrid rule-based and machine learning pipeline that successfully maps unstructured military working dog medical records to standardized SNOMED CT concepts with 82.7% agreement to expert consensus, thereby enabling automated population-level health surveillance.