# Extracting Clinical Entities

Run clinical and biomedical named-entity recognition on medical text with OpenMed's...

- Repository: https://github.com/maziyarpanahi/openmed (5755 stars)
- Category: Writing
- License: Apache-2.0
- Last updated: 2026-10-31
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities -g -a claude-code -y`
Codex: `npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities -g -a codex -y`
Cursor: `npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities -g -a cursor -y`
Gemini CLI: `npx skills add maziyarpanahi/openmed --skill extracting-clinical-entities -g -a gemini-cli -y`
Claude Code plugin marketplace: `/plugin marketplace add maziyarpanahi/openmed`

## Description

Run clinical and biomedical named-entity recognition on medical text with OpenMed's analyze_text. Use when the user wants to extract diseases, drugs, anatomy, genes, or other biomedical entities from notes; needs NER output as dict/json/html/csv; wants to filter by confidence, group entities, toggle sentence detection, or save spans to JSONL; or wants the openmed analyze CLI. Pairs with loading-openmed-models and choosing-openmed-models, and runs after deidentifying-clinical-text in a privacy-first pipeline.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# 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
    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:

```text
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`:

The rest: https://github.com/maziyarpanahi/openmed/blob/HEAD/skills/extracting-clinical-entities/SKILL.md