extracting-clinical-entities

npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities --agent claude-code

Same command for any agent — swap --agent for codex, cursor, copilot.

# 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 ```bash pip install "openmed[hf]" ``` ## Quick start ```python 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 confidenc

pip install "openmed[hf]"
openmed analyze --text "Type 2 diabetes managed with metformin." \
Or analyze a file:
openmed analyze --input-file note.txt --model disease_detection_superclinical -o csv