Quality Assurance of UMLS Semantic Type Assignments Using SNOMED CT Hierarchies
Bibliographic record
Abstract
BACKGROUND: The Unified Medical Language System (UMLS) is one of the largest biomedical terminological systems, with over 2.5 million concepts in its Metathesaurus repository. The UMLS's Semantic Network (SN) with its collection of 133 high-level semantic types serves as an abstraction layer on top of the Metathesaurus. In particular, the SN elaborates an aspect of the Metathesaurus's concepts via the assignment of one or more types to each concept. Due to the scope and complexity of the Metathesaurus, errors are all but inevitable in this semantic-type assignment process. OBJECTIVES: To develop a semi-automated methodology to help assure the quality of semantic-type assignments within the UMLS. METHODS: The methodology uses a cross-validation strategy involving SNOMED CT's hierarchies in combination with UMLS semantic types. Semantically uniform, disjoint concept groups are generated programmatically by partitioning the collection of all concepts in the same SNOMED CT hierarchy according to their respective semantic-type assignments in the UMLS. Domain experts are then called upon to review the concepts in any group having a small number of concepts. It is our hypothesis that a semantic-type assignment combination applicable only to a very small number of concepts in a SNOMED CT hierarchy is an indicator of potential problems. RESULTS: The methodology was applied to the UMLS 2013AA release along with the SNOMED CT from January 2013. An overall error rate of 33% was found for concepts proposed by the quality-assurance methodology. Supporting our hypothesis, that number was four times higher than the error rate found in control samples. CONCLUSION: The results show that the quality-assurance methodology can aid in effective and efficient identification of UMLS semantic-type assignment errors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.126 | 0.246 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".