ClinicalMap25-for-SnomedCT

ClinicalMap25-for-SnomedCT provides a dense, token-level mapping of SnomedCT clinical concepts to the ClinicalEncoder25 embedding space. This resource enables fast, interpretable semantic search and reasoning over medical terminology, directly compatible with the ClinicalEncoder25 model.

---

Key Features

• 707,574 SnomedCT Concepts: Each concept is represented as a 1024-dimensional embedding, normalized for efficient cosine similarity computation.
• Optimized for Speed: Embeddings are stored as 8-bit floats, enabling fast matrix multiplication on supported hardware.
• Padding for Efficiency: 10 extra zero-padded rows ensure compatibility with optimized matrix operations (e.g., torch._scaled_mm).
• Ready for Integration: Designed for use with ClinicalEncoder25.

---

Files

File Name	Description
clinical_map_25_for_sct_concepts.pth	707,584 rows (707,574 concepts + 10 padding) of 1024 8-bit floats, normalized for cosine similarity.
sct_concepts.txt	707,574 SnomedCT concept names, one per line. May contain duplicates.

---

Usage

1. Load the Embeddings

# %%
import torch

# %%
# Load the embeddings (8-bit floats)
concept_vectors = torch.load("clinical_map_25_for_sct_concepts.pth", map_location="cpu")

# If your hardware doesn't support FP8, cast to float16
concept_vectors = concept_vectors.to(torch.float16)

2. Load the Concept Names

# %%
# Load concept names
with open("sct_concepts.txt", "r") as f:
    concept_names = [line.strip() for line in f.readlines()]

3. Perform Semantic Search

# %%
# Load the ClinicalEncoder25 model
from transformers import AutoModel, AutoTokenizer
MODEL_NAME = "Parallia/ClinicalEncoder25-Diagnosable-Colbert-L2-for-medical-texts"
model, tokenizer = AutoModel.from_pretrained(MODEL_NAME), AutoTokenizer.from_pretrained(MODEL_NAME)

# %%
# Tokenize your document
doc = "The patient suffers from PAPA syndrome. The patient therefore takes NSAIDs daily, as prophylaxis."
doc_inputs = tokenizer(
    doc,
    return_tensors="pt",
    add_special_tokens=True,
)
doc_tokens = [
    tokenizer.decode([tid], clean_up_tokenization_spaces=False)
    for tid in doc_inputs["input_ids"][0].tolist()
]

# %%
# Encode your document
with torch.no_grad():
    doc_inputs = {k: v.to(model.device) for k, v in doc_inputs.items()}
    doc_outputs = model(**doc_inputs)
    doc_vectors = doc_outputs.last_hidden_state[0]

# Normalize the token embeddings for easier cosine similarity scoring
doc_vectors = (doc_vectors / doc_vectors.norm(dim=1, keepdim=True).clamp_min(1e-12))

# Ensure the query and embeddings are on the same device, and use the same dtype
doc_vectors = doc_vectors.to(concept_vectors.device).to(concept_vectors.dtype)

# %%
# Compute cosine similarity
if concept_vectors.dtype != torch.float8_e4m3fn:
    similarities = torch.mm(doc_vectors, concept_vectors.t())
else:
    unit_vector = torch.tensor(1.0, device=concept_vectors.device, dtype=torch.float32)
    similarities = torch._scaled_mm(
        doc_vectors, concept_vectors.T,
        unit_vector, unit_vector,
        out_dtype=torch.float16
    )

# %%
# Retrieve concept names of the best matches per token, and display their similarity scores
how_many_to_display = 3
for i, token in enumerate(doc_tokens[1:]):
  print(f"\nToken #{i} '{token}':")
  top_indices = similarities[i].topk(how_many_to_display, largest=True, sorted=True).indices
  for idx in top_indices:
      print(f"- {concept_names[idx]} ({similarities[0, idx].item()}")

---

Notes

• Padding Rows: The last 10 rows are zero-padded and will never match any query.
• Memory Efficiency: Use FP8 for minimal memory usage. If FP8 is not supported, use FP16.
• SnomedCT Choice: SnomedCT was selected for its balance between medical coverage and ontology size, compared to UMLS (larger) and ICD-10 (smaller).
• Duplicates: Concept names may contain duplicates; this map is for demonstration purposes.

---

License

This dataset is released under the CC-BY-NC 4.0 license. For commercial use, please obtain a license. You might also need a SnomedCT license if you intend to map the output of this script to the SnomedCT ontology.

---

References

• ClinicalEncoder25 Model Card
• SnomedCT