OpenMed-NER-PathologyDetect-PubMed-335M

What it does

This model detects disease entities in English clinical notes and biomedical literature, assigning each mention the label Disease. It is built on BiomedBERT with 334M parameters and reports an F1 score of 0.909 on the NCBI Disease benchmark, along with precision of 0.891 and recall of 0.927.

Best for

• Disease mentions in clinical notes
• Disease extraction from research papers
• Disease tagging in biomedical literature

What it detects (1 labels)

• Disease

Quality

Disease Entity Recognition - Disease entities from the NCBI dataset

Use it

pip install transformers torch

from transformers import pipeline
ner = pipeline("token-classification", model="OpenMed/OpenMed-NER-PathologyDetect-PubMed-335M", aggregation_strategy="simple")
print(ner("Patient prescribed 10 mg aspirin for hypertension."))

pip install --upgrade "openmed[onnx-runtime]"

from openmed import OnnxModel
model = OnnxModel.from_pretrained("OpenMed/OpenMed-NER-PathologyDetect-PubMed-335M-v1-onnx-android")
for entity in model("The patient started imatinib after the consultation."):
    print(entity.to_dict())

pip install "openmed[mlx]"

from openmed import analyze_text
from openmed.core.config import OpenMedConfig
result = analyze_text("Patient started imatinib in March.", model_name="OpenMed/OpenMed-NER-PathologyDetect-PubMed-335M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)