extracting-clinical-entities SKILL.md skillA

extracting-clinical-entities from maziyarpanahi/openmed is an agent skill: a SKILL.md folder an AI agent loads when a task calls for it.

Install it, pinned
mdr add maziyarpanahi/openmed/extracting-clinical-entities@v1.0

Pins v1.0 in mdr.lock. Pin the fingerprint instead.

Audit grade
A, 17 of 17 checks passed
Kind
SKILL.md skill
Repository
maziyarpanahi/openmed
Path
skills/extracting-clinical-entities/SKILL.md
Versions recorded
1
Latest version
v1.0, committed Jul 20, 2026
Size
7,714 bytes
Repository stars
5,784
License
Apache-2.0

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…

Collected from public GitHub. Every version is saved exactly as it was and never changes. Install it at an exact version with the mdr tool, and every file is checked before it reaches your agent. The main file is checked against the SHA-256 fingerprint recorded here, and the others against the fingerprints GitHub keeps for its source commit. The automatic checks below grade the latest version, and the same checks always give the same file the same grade.

What the file says

# 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
…

Read the whole file at its exact version.

How to install

The exact contents, by fingerprint
mdr add maziyarpanahi/openmed/extracting-clinical-entities@sha256:59394e842bd79a06

The line at the top of this page pins the label v1.0, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.

Badge

mdr badge

[![mdr](https://markdownregistry.com/badge/art_rrmgget7momcy4wm.svg)](https://markdownregistry.com/a/art_rrmgget7momcy4wm)

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Versions

The registry has recorded 1 version of this file, committed on Jul 20, 2026.

versioncommittedcommitsizeaudit
v1.0 latest2026-07-20 74f7d6b 7,714 BA view

Audit of the latest version

A  17 of 17 checks passed. Fixed automatic checks with no AI involved, so the same checks give the same answer every run.
  • pass: Frontmatter block present
  • pass: Frontmatter declares a name
  • pass: Frontmatter declares a description
  • pass: Size between 200 bytes and 200 KB (7714 bytes)
  • pass: No zero-width or bidi control characters
  • pass: No instruction hidden inside an HTML comment
  • pass: No link to an exfiltration or paste host
  • pass: No credential-shaped string
  • pass: No instruction to send local credentials anywhere
  • pass: No text hidden with inline styles
  • pass: No prompt-injection phrasing
  • pass: No curl or wget piped into a shell
  • pass: No recursive delete of root, home or parent
  • pass: No instruction to read or print local credentials
  • pass: No base64 blob over 200 characters
  • pass: No link to a raw IP address
  • pass: No script tag

Source

GitHub

maziyarpanahi/openmed · 5,784 stars · license Apache-2.0 · pushed 2026-11-07 · branch master

API

GET https://markdownregistry.com/api/v1/artifacts/art_rrmgget7momcy4wm
GET https://markdownregistry.com/api/v1/resolve?ref=maziyarpanahi/openmed/extracting-clinical-entities
GET https://markdownregistry.com/api/v1/blob/59394e842bd79a06fb876ecfc3c61da6e3e86c0d5a3bf3dc93f575352ecfd4dc

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