DiscHPO: Generative Models and Sentence Transformers for the Recognition and Normalisation of Continuous and Discontinuous Phenotype Mentions
Bibliographic record
Abstract
Background: Extracting genetic phenotype mentions from clinical reports and normalising them to standardised concepts within the HPO ontology are essential for consistent interpretation and representation of genetic conditions. This is particularly important in fields such as dysmorphology and plays a key role in advancing personalised healthcare. However, modern clinical Named Entity Recognition (NER) methods face challenges in accurately identifying discontinuous mentions (i.e., entity spans that are interrupted by unrelated words) which can be found in these clinical reports. Objective: This study aims to develop a system that can accurately extract and normalise genetic phenotypes, specifically from physical examination reports related to dysmorphology assessment. These mentions appear in both continuous and discontinuous lexical forms, with a focus on addressing challenging disjoint (discontinuous) entity spans. Methods: We introduce DiscHPO, a two-phase pipeline consisting of (1) a sequence-to-sequence NER model for span extraction, and (2) an entity normaliser that employs a Sentence Transformer bi-encoder for candidate generation and a crossencoder re-ranker for selecting the best candidate as the normalised concept. This system was tested as part of our participation in Track 3 of the BioCreative VIII shared task. Results: For overall performance on the test set, the top-performing model for entity normalisation achieved an F1 score of 0.7229, while the best span extraction model reached an F1 score of 0.6647. Both scores surpassed those of two baseline models using the same dataset, indicating superior efficacy in handling both continuous and discontinuous spans. Approximately 14% of entity mentions in the dataset are disjoint spans. On the validation set, we were able to demonstrate our system's ability to recognise these mentions, with the model achieving an F1 score of 0.6235 for exact match on discontinuous spans only. Conclusions: The findings suggest that exact extraction of entity spans may not always be necessary for successful normalisation. Partial mention matches can be sufficient as long as they capture the essential concept information, supporting the system’s utility in clinical downstream tasks.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".