DT4H/CardioBERTa.en_P_enriched

DT4H_CardioBERTa_parents_en_enriched

DT4H_CardioBERTa_parents_en_enriched is a English biomedical terminology encoder for clinical concept normalization and entity linking. It is initialized from [DT4H/CardioBERTa.en] and specialized using CUI-supervised terminology pairs and metric learning.

Backbone

The backbone belongs to the CardioBERTa family from CardioLM - a multilingual suite of small language models for the cardiology domain. CardioBERTa comprises language-specific encoder models adapted to cardiology through continued pretraining on monolingual biomedical and cardiology-related corpora using Masked Language Modeling (MLM). The family covers Czech, Dutch, English, Italian, Romanian, Spanish and Swedish.

Training

Language	English (en)
Triplet collection	enriched
Strategy	parents
Objective	Multi-Similarity Loss
Mining	All triplets, margin 0.2
Pooling	CLS
Epochs	1
Batch size	256
Learning rate	2e-5
Max. length	25

CUI-supervised terminology pairs enriched with parent-level ontology relations.

Terminology statistics

Strategy	Triplets	CUIs	Unique terms	Unique positives	Terms/CUI	Δ terms
synonyms	83,914	83,914	165,661	83,472	2.00	0
parents	1,699,553	477,290	550,651	432,552	4.03	+384,990
grandparents	4,952,020	477,293	550,651	485,607	10.06	+384,990

This model uses 1,699,553 triplets, covering 477,290 CUIs and 550,651 unique normalized terms.

The training terminology is not distributed with this repository because it contains resources subject to UMLS licensing conditions. Only aggregate statistics are released.

Intended use

The model is intended for terminology embedding, biomedical candidate retrieval, concept normalization and entity linking, particularly in cardiology and clinical NLP pipelines. It is not intended for direct clinical decision-making.

Usage

import torch
import torch.nn.functional as F
from transformers import AutoModel, AutoTokenizer

model_id = "DT4H/DT4H_CardioBERTa_parents_en_enriched"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModel.from_pretrained(model_id)

inputs = tokenizer(
    "clinical concept",
    return_tensors="pt",
    truncation=True,
    max_length=25,
)

with torch.no_grad():
    output = model(**inputs)

embedding = F.normalize(
    output.last_hidden_state[:, 0, :],
    p=2,
    dim=1,
)

Reference

Danu et al. CardioLM - a multilingual suite of small language models for the cardiology domain.

Developed within the DataTools4Heart (DT4H) project, Grant Agreement 101057849.