THIRAWAT-BioLORD is a fine-tuned ColBERTv1-style late-interaction reranker for drug terminology mapping to standardized OMOP Drug concepts, especially RxNorm and RxNorm Extension.
THIRAWAT stands for Terminology Harmonization using Late-Interaction Reranker With Alignment-tuned Transformers. This model is designed to rerank candidate drug concepts generated by a first-stage retriever, not to perform full terminology mapping by itself.
This checkpoint is initialized from BioLORD-2023, a biomedical sentence embedding model trained to produce semantic representations for clinical sentences and biomedical concepts using definitions, biomedical knowledge graph information, and LLM-generated clinical terminology definitions.
THIRAWAT-BioLORD was evaluated as part of the full THIRAWAT Mapper pipeline. In plain terms:
Hits@1 means the correct mapping was ranked first.
Hits@3 means the correct mapping appeared in the top 3 suggestions, which is useful for human review.
Across the evaluated datasets, the full BioLORD-based pipeline ranked the correct drug concept first in 82.0%–85.8% of cases, and placed it within the top 3 in 87.1%–93.4% of cases.
The table below compares the full SapBERT-based and BioLORD-based THIRAWAT Mapper pipelines using reranking plus deterministic tie-breaking.
Dataset
Pipeline / Reranker
Correct at #1
Correct in top 3
Difference vs THIRAWAT-SapBERT at #1
Difference vs THIRAWAT-SapBERT top 3
Branded Drugs
THIRAWAT-SapBERT
94.2%
96.4%
baseline
baseline
Branded Drugs
THIRAWAT-BioLORD
82.0%
87.1%
-12.2 pp
-9.3 pp
Clinical Drugs
THIRAWAT-SapBERT
85.9%
93.0%
baseline
baseline
Clinical Drugs
THIRAWAT-BioLORD
85.2%
92.3%
-0.7 pp
-0.7 pp
Thai Medicines Terminology
THIRAWAT-SapBERT
86.8%
95.4%
baseline
baseline
Thai Medicines Terminology
THIRAWAT-BioLORD
85.8%
93.4%
-1.0 pp
-2.0 pp
These are full-pipeline results, not standalone checkpoint-only results. The BioLORD-based pipeline used BioLORD-2023 retrieval, THIRAWAT-BioLORD reranking with BiMaxSim, and deterministic tie-breaking. The deterministic tie-breaker is implemented in THIRAWAT Mapper, not inside this Hugging Face model checkpoint.
In this evaluation, THIRAWAT-SapBERT was stronger overall, especially for Branded Drugs. THIRAWAT-BioLORD remained competitive on Clinical Drugs and Thai Medicines Terminology, where the gap at Hits@1 was approximately 1 percentage point or less.
Paper
This model is described in:
Efficient Drug Terminology Mapping with Bidirectional Late-Interaction Reranking and Deterministic Reordering
Healthcare Informatics Research, 2026;32(2):156–165
DOI: https://doi.org/10.4258/hir.2026.32.2.156
Please cite the paper if you use this model or THIRAWAT Mapper.
Intended Use
Use this model for reranking candidate OMOP/RxNorm drug concepts in medication terminology mapping workflows.
Typical pipeline:
Retrieve candidate concepts with BioLORD-2023 or another biomedical retriever.
Rerank candidates with THIRAWAT-BioLORD.
Score late-interaction matches with inference-time BiMaxSim.
Apply deterministic tie-breaking for near-ties using drug-specific cues such as strength, dosage form/route, release characteristics, and brand annotations.
Review the final mappings before production use.
This model is intended for terminology mapping and mapping assistance. It is not a prescribing tool, medication safety checker, or standalone clinical decision support model.
Primary domain: OMOP Drug mapping to RxNorm / RxNorm Extension
Relationship to THIRAWAT-SapBERT
THIRAWAT-BioLORD and THIRAWAT-SapBERT use the same THIRAWAT late-interaction reranking approach, but start from different biomedical encoders.
THIRAWAT-SapBERT is initialized from SapBERT-XLMR, which is optimized for biomedical entity linking and synonym alignment.
THIRAWAT-BioLORD is initialized from BioLORD-2023, which emphasizes biomedical semantic similarity grounded in definitions and knowledge graph descriptions.
In the reported drug-mapping evaluation, THIRAWAT-SapBERT achieved the strongest overall full-pipeline results. THIRAWAT-BioLORD is provided as an alternative checkpoint with a different biomedical representation bias and may be useful for comparison, ablation, or deployment settings where BioLORD-style semantic representations are preferred.
Limitations
Evaluated primarily for drug terminology mapping.
Not yet validated for other OMOP domains such as Condition, Procedure, Measurement, or Observation.
Performance depends on the target vocabulary and ATHENA/RxNorm coverage.
BioLORD-based retrieval showed lower retriever-only performance than SapBERT-XLMR in the reported drug-mapping setting, although reranking and deterministic tie-breaking substantially narrowed the gap.
Local brands may be mapped to generic ingredient-strength-form concepts when no corresponding branded target exists.
Remaining errors often involve clinically close candidates, such as strength, form, release, combination-product, or brand-coverage mismatches.
Automated mappings should be reviewed before use in production OMOP ETL or research workflows.
Citation
bibtex
1@article{adulyanukosol2026thirawat,
2 title = {Efficient Drug Terminology Mapping with Bidirectional Late-Interaction Reranking and Deterministic Reordering},
3 author = {Adulyanukosol, Natthawut and Chaisutyakorn, Krittaphas and Sombutjaroan, Saknarong and Kanjanapong, Suchanan and Suriyaphol, Prapat},
4 journal = {Healthcare Informatics Research},
5 year = {2026},
6 volume = {32},
7 number = {2},
8 pages = {156--165},
9 doi = {10.4258/hir.2026.32.2.156}
10}
If you use the BioLORD-2023 base model directly, please also cite the original BioLORD-2023 paper.
bibtex
1@article{remy2024biolord,
2 title = {BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights},
3 author = {Remy, François and Demuynck, Kris and Demeester, Thomas},
4 journal = {Journal of the American Medical Informatics Association},
5 year = {2024},
6 volume = {31},
7 number = {9},
8 pages = {1844--1855},
9 doi = {10.1093/jamia/ocae029}
10}
