OpenMed-NER-PathologyDetect-SuperMedical-355M

What it does

This model identifies disease entities in English clinical notes, research papers, and healthcare documents. It is a 354M-parameter RoBERTa model that reports an F1 score of 0.876 on the NCBI Disease benchmark, with precision of 0.869 and recall of 0.883.

Best for

• Disease mentions in clinical notes
• Literature mining for disease names
• Patient records disease extraction

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-SuperMedical-355M", 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-SuperMedical-355M-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-SuperMedical-355M", config=OpenMedConfig(backend="mlx"))
for e in result.entities:
    print(e.label, e.text)