--- base_model: FremyCompany/BioLORD-2023 pipeline_tag: sentence-similarity --- # THIRAWAT-BioLORD **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. It is best used through **THIRAWAT Mapper**: https://github.com/sidataplus/THIRAWAT-mapper ## Evaluation Summary 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: 1. Retrieve candidate concepts with BioLORD-2023 or another biomedical retriever. 2. Rerank candidates with THIRAWAT-BioLORD. 3. Score late-interaction matches with inference-time **BiMaxSim**. 4. Apply deterministic tie-breaking for near-ties using drug-specific cues such as strength, dosage form/route, release characteristics, and brand annotations. 5. 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. ## Example: THIRAWAT Mapper CLI ```bash pip install thirawat-mapper ``` Interactive query: ```bash thirawat infer query \ --db data/lancedb/db \ --table concepts_drug \ --device auto \ --reranker-id sidataplus/THIRAWAT-BioLORD ``` Bulk mapping: ```bash thirawat infer bulk \ --db data/lancedb/db \ --table concepts_drug \ --input data/usagi.csv \ --out runs/mapping \ --candidate-topk 200 \ --n-limit 20 \ --device auto \ --reranker-id sidataplus/THIRAWAT-BioLORD ``` ## Model Details * **Base model:** `FremyCompany/BioLORD-2023` * **Architecture:** ColBERTv1-style late-interaction reranker * **Model type:** PyLate ColBERT model * **Training objective:** one-sided MaxSim * **Recommended inference scoring:** BiMaxSim * **Projection dimension:** 128 * **Max query length:** 96 tokens * **Max candidate length:** 96 tokens * **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 @article{adulyanukosol2026thirawat, title = {Efficient Drug Terminology Mapping with Bidirectional Late-Interaction Reranking and Deterministic Reordering}, author = {Adulyanukosol, Natthawut and Chaisutyakorn, Krittaphas and Sombutjaroan, Saknarong and Kanjanapong, Suchanan and Suriyaphol, Prapat}, journal = {Healthcare Informatics Research}, year = {2026}, volume = {32}, number = {2}, pages = {156--165}, doi = {10.4258/hir.2026.32.2.156} } ``` If you use the BioLORD-2023 base model directly, please also cite the original BioLORD-2023 paper. ```bibtex @article{remy2024biolord, title = {BioLORD-2023: semantic textual representations fusing large language models and clinical knowledge graph insights}, author = {Remy, François and Demuynck, Kris and Demeester, Thomas}, journal = {Journal of the American Medical Informatics Association}, year = {2024}, volume = {31}, number = {9}, pages = {1844--1855}, doi = {10.1093/jamia/ocae029} } ```