Extracting Clinical Entities

openmed.analyze_text runs a token-classification model over medical text and returns structured entities with character offsets and confidence scores. It runs on-device after a one-time model download.

When to use

• Pull diseases, medications, anatomy, genes, proteins, etc. out of clinical text.
• You need exact character spans (start/end) plus confidence per entity.
• You want output as objects, JSON, an HTML highlight view, or CSV.
• You are building the "extract entities" stage of a clinical NLP pipeline.

To choose a model, see choosing-openmed-models. To load it once and reuse it, see loading-openmed-models. In a PHI workflow, de-identify first (see deidentifying-clinical-text), then run NER on the redacted text.

Install

pip install "openmed[hf]"

Quick start

import openmed

note = (
    "Patient prescribed 500 mg metformin for type 2 diabetes mellitus. "
    "Reports intermittent chest pain; ruled out myocardial infarction."
)

result = openmed.analyze_text(
    note,
    model_name="disease_detection_superclinical",  # registry key, HF id, or local path
    output_format="dict",                           # dict | json | html | csv
    confidence_threshold=0.5,
)

for ent in result.entities:
    print(f"{ent.label:12} {ent.text!r:40} {ent.confidence:.2f} [{ent.start}:{ent.end}]")

With output_format="dict" you get a PredictionResult. The fields you use most:

result.text          # the original input text
result.entities      # list of entity objects
result.model_name    # which model produced these
ent.text             # the surface string
ent.label            # entity type, e.g. "DISEASE"
ent.confidence       # model score in [0, 1]   (NOTE: .confidence, not .score)
ent.start / ent.end  # character offsets into result.text

Output formats

analyze_text(...) returns different types depending on output_format: