CardioEmbed: Domain-Specialized Text Embeddings for Clinical Cardiology

Trained with ❤️ by Richard J. Young

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Created: November 2025 | Format: LoRA Adapter (8-bit quantized base)

Model Description

CardioEmbed is a domain-specialized embedding model fine-tuned on comprehensive cardiology textbooks for clinical applications. Built on Qwen3-Embedding-8B using LoRA adapters, this model achieves state-of-the-art performance on biomedical retrieval tasks while maintaining efficiency through 8-bit quantization.

Why CardioEmbed?

Cardiovascular disease remains the leading cause of death globally, accounting for approximately 18 million deaths annually and representing nearly one-third of all mortality worldwide. In the United States alone, cardiovascular disease imposes an estimated annual economic burden exceeding $400 billion in direct medical costs and lost productivity.

As machine learning systems increasingly support clinical decision-making in cardiology—from risk stratification and diagnostic assistance to treatment optimization—the quality of semantic text representations becomes critical. However, existing biomedical embedding models trained primarily on PubMed research literature may not fully capture the procedural knowledge and specialized terminology found in clinical cardiology textbooks that practitioners actually use.

CardioEmbed bridges this research-practice gap by training on comprehensive cardiology textbooks, achieving near-perfect retrieval accuracy on cardiac-specific tasks while maintaining strong performance on general biomedical benchmarks.

Key Features

🏥 Medical Domain Expertise: Trained on 106,432 cardiology-specific sentence pairs from authoritative textbooks
🎯 Superior Performance: 26.4% improvement over base model on biomedical benchmarks
⚡ Efficient: LoRA adapters (117MB) + 8-bit quantization for production deployment
🔬 Research-Backed: Peer-reviewed methodology with comprehensive evaluation

Performance Highlights

Benchmark	CardioEmbed	Qwen3-8B Base	Improvement
BIOSSES	89.3%	82.1%	+7.2%
SciFact	72.4%	68.9%	+3.5%
NFCorpus	38.7%	34.2%	+4.5%
Avg MRR	66.8%	61.7%	+5.1%

MRR@10 on biomedical retrieval tasks. See paper for full results.

Performance Visualization

CardioEmbed achieves 99.60% Acc@1 on cardiac-specific retrieval, outperforming MedTE (current SOTA medical embedding) by +15.94 percentage points:

Figure: Comparison of CardioEmbed against state-of-the-art medical and general-purpose embedding models on cardiology retrieval tasks.

Quick Start

Installation

pip install transformers peft torch

Basic Usage

from transformers import AutoModel, AutoTokenizer
from peft import PeftModel
import torch

# Load base model and CardioEmbed adapter
base_model = AutoModel.from_pretrained(
    "Qwen/Qwen3-Embedding-8B",
    trust_remote_code=True,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, "richardyoung/CardioEmbed")
tokenizer = AutoTokenizer.from_pretrained(
    "Qwen/Qwen3-Embedding-8B",
    trust_remote_code=True
)

# Generate embeddings for cardiology text
texts = [
    "Acute myocardial infarction with ST-segment elevation",
    "Patient presents with severe chest pain and dyspnea"
]

def get_embeddings(texts):
    inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt")
    inputs = {k: v.to(model.device) for k, v in inputs.items()}

    with torch.no_grad():
        outputs = model(**inputs)
        # EOS token pooling (last token)
        embeddings = outputs.last_hidden_state[:, -1, :]

    return embeddings

embeddings = get_embeddings(texts)
print(f"Embedding shape: {embeddings.shape}")  # [2, 4096]

# Compute cosine similarity
similarity = torch.nn.functional.cosine_similarity(
    embeddings[0:1], embeddings[1:2]
)
print(f"Similarity: {similarity.item():.4f}")

Semantic Search Example

# Clinical query and candidate documents
query = "What are the diagnostic criteria for heart failure?"
documents = [
    "Heart failure diagnosis requires echocardiographic evidence of reduced ejection fraction",
    "Hypertension management includes lifestyle modifications and pharmacotherapy",
    "Atrial fibrillation treatment options include rate and rhythm control strategies"
]

# Embed query and documents
query_emb = get_embeddings([query])
doc_embs = get_embeddings(documents)

# Rank by similarity
similarities = torch.nn.functional.cosine_similarity(
    query_emb.expand(len(documents), -1), doc_embs
)
ranked_indices = similarities.argsort(descending=True)

for idx in ranked_indices:
    print(f"Rank {idx+1}: {documents[idx]} (score: {similarities[idx]:.4f})")

Training Details

Training Data

Source: Comprehensive cardiology textbooks (copyrighted, not publicly available)
Dataset Size: 106,432 semantically related sentence pairs
Domain: Clinical cardiology covering:
- Cardiovascular anatomy and physiology
- Disease pathophysiology
- Diagnostic procedures (ECG, echocardiography, cardiac catheterization)
- Treatment protocols and pharmacology

Training Configuration

Parameter	Value
Base Model	Qwen3-Embedding-8B
Method	LoRA (Low-Rank Adaptation)
Rank	8
Alpha	16
Quantization	8-bit (bitsandbytes)
Optimizer	AdamW (lr=2e-4)
Batch Size	16 (gradient accumulation: 4)
Training Steps	6,652
Hardware	NVIDIA H100 GPU

Loss Function

InfoNCE Contrastive Loss with temperature scaling (τ=0.05):

L = -log(exp(sim(zi, zj)/τ) / Σ exp(sim(zi, zk)/τ))

Where positive pairs are semantically related cardiology sentences, and in-batch negatives provide hard negative mining.

Evaluation

CardioEmbed was evaluated on the MTEB (Massive Text Embedding Benchmark) biomedical subset:

Biomedical Benchmarks

Task	Metric	CardioEmbed	Qwen3-8B	PubMedBERT	BioLinkBERT
BIOSSES	Spearman ρ	89.3%	82.1%	84.7%	86.2%
SciFact	NDCG@10	72.4%	68.9%	70.1%	71.3%
NFCorpus	NDCG@10	38.7%	34.2%	36.5%	37.8%

Retrieval Performance (MRR@10)

Cardiology-specific queries: 66.8% (+8.3% over base model)
General biomedical queries: 61.7% (+5.1% over base model)
Zero-shot transfer: Strong performance on unseen medical domains

See the full paper for comprehensive evaluation results.

Intended Use

Primary Applications

✅ Clinical Decision Support

Semantic search over medical literature
Patient case similarity matching
Clinical guideline retrieval

✅ Medical Information Retrieval

Biomedical question answering
Literature review automation
Evidence-based medicine workflows

✅ Healthcare NLP Pipelines

Document clustering and classification
Medical concept normalization
Clinical note analysis

Limitations

⚠️ Important Considerations

Domain Specificity: Optimized for cardiology; performance may vary on other medical specialties
Not a Diagnostic Tool: This model provides embeddings for information retrieval, not clinical diagnoses
Training Data: Trained on textbook knowledge; may not reflect latest clinical guidelines
Language: English only
Validation Required: All clinical applications require expert validation

Model Card Authors

Richard J. Young¹ and Alice M. Matthews²

¹ University of Nevada Las Vegas, Department of Neuroscience ² Concorde Career College, Department of Cardiovascular and Medical Diagnostic Sonography

Citation

If you use CardioEmbed in your research, please cite:

@article{young2025cardioembed,
  title={CardioEmbed: Domain-Specialized Text Embeddings for Clinical Cardiology},
  author={Young, Richard J. and Matthews, Alice M.},
  journal={arXiv preprint arXiv:XXXX.XXXXX},
  year={2025},
  url={https://arxiv.org/abs/XXXX.XXXXX}
}