Evaluating Clinical Concept Extraction and Evidence-Bounded Terminology Linking: Multisite Model Comparison and Pilot Ablation Study

Abstract

Background: Clinical terminology pipelines must first extract candidate spans from narrative notes and then determine whether those spans map to existing concepts or warrant further review. Evaluation is difficult because span boundaries vary between annotators and because downstream decisions depend on the terminology evidence retrieved for each span. Objective: We evaluated clinical concept extraction, terminology linking across controlled evidence conditions, and ontology-extension triage for terms that remained unmatched after initial terminology screening. Methods: We conducted 3 complementary pilot evaluations that used distinct units of analysis and were analyzed separately. Study 1 compared 5 automated extraction pipelines and a union-merge analysis with 2 human annotation sets in 66 deidentified clinical notes from 3 health systems. Agreement was evaluated by exact string matching and BGE-large-en-v1.5 embedding matching. Study 2 evaluated 56 clinical spans, including 28 with reference Unified Medical Language System concepts and 28 adjudicated as unsuitable for ontology extension, under complete retrieval, matched-concept masking, and large language model-only inference, yielding 168 span-condition outputs. The graph retrieval pipeline used BGE-large-en-v1.5 embeddings, and the decision model was Gemma 3 27B. Study 3 applied full vector retrieval to 84 terms previously not matched in either UMLS or BioPortal. Results: In Study 1, interannotator exact-match F1 was 0.29 and embedding-match F1 was 0.75. Automated exact-match F1 scores ranged from 0.07 to 0.17; embedding-match F1 was highest for MedGemma (0.55), followed by Gemma (0.53), sci_md and SciBERT (each 0.43), and Llama 3.3 (0.32). In Study 2, complete retrieval returned a reference-matched link for 28/28 known-concept spans (100%; 95% CI, 87.9%-100%). Masking assigned POSSIBLE_CANDIDATES to all 28; large language model-only inference assigned POSSIBLE_CANDIDATES to 25/28 (89.3%) and LINKED to 3/28 (10.7%). Across the 3 evidence conditions, the same 12/28 unsuitable-extension spans were classified as NOT_MEANINGFUL (42.9%) and the same 16/28 as POSSIBLE_CANDIDATES (57.1%). In Study 3, the pipeline assigned PLAUSIBLE_EXISTING_CONCEPT to all 84 terms, none was flagged for extension, and top-candidate similarity averaged 0.914 (SD 0.027); extension status was not independently adjudicated. Conclusions: Measured extraction performance varied substantially by matching definition, whereas exact-link decisions varied with the availability of matched terminology evidence. In the follow-up sample, initial nonmatching did not establish ontology novelty: after semantic retrieval, the pipeline classified all 84 terms as plausible existing concepts and proposed none for extension. These findings support separate evaluation of extraction, retrieval, evidence-grounded linking, and extension candidacy.