| --- |
| base_model: FremyCompany/BioLORD-2023 |
| pipeline_tag: sentence-similarity |
| --- |
| |
| # THIRAWAT-BioLORD |
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| **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. |
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| 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. |
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| 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. |
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| It is best used through **THIRAWAT Mapper**: |
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| https://github.com/sidataplus/THIRAWAT-mapper |
|
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| ## Evaluation Summary |
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| THIRAWAT-BioLORD was evaluated as part of the full **THIRAWAT Mapper** pipeline. In plain terms: |
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| * **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. |
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| 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. |
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| The table below compares the full SapBERT-based and BioLORD-based THIRAWAT Mapper pipelines using reranking plus deterministic tie-breaking. |
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| | 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 | |
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| 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. |
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| 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. |
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| ## Paper |
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| This model is described in: |
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| **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 |
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| Please cite the paper if you use this model or THIRAWAT Mapper. |
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| ## Intended Use |
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| Use this model for reranking candidate OMOP/RxNorm drug concepts in medication terminology mapping workflows. |
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| Typical pipeline: |
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| 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. |
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| 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. |
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| ## Example: THIRAWAT Mapper CLI |
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| ```bash |
| pip install thirawat-mapper |
| ``` |
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| Interactive query: |
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| ```bash |
| thirawat infer query \ |
| --db data/lancedb/db \ |
| --table concepts_drug \ |
| --device auto \ |
| --reranker-id sidataplus/THIRAWAT-BioLORD |
| ``` |
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| Bulk mapping: |
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| ```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 |
| ``` |
|
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| ## Model Details |
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| * **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 |
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| ## Relationship to THIRAWAT-SapBERT |
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| THIRAWAT-BioLORD and THIRAWAT-SapBERT use the same THIRAWAT late-interaction reranking approach, but start from different biomedical encoders. |
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| * **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. |
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| 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. |
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| ## Limitations |
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| * 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. |
|
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| ## Citation |
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| ```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} |
| } |
| ``` |
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| If you use the BioLORD-2023 base model directly, please also cite the original BioLORD-2023 paper. |
|
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| ```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} |
| } |
| ``` |
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