Who it is forA developer new to clinical NLP who wants a working pipeline without learning every model name.
The problemPicking models, chaining de-identification and NER, and wiring outputs takes research time.
The outcomeAn on-device pipeline that de-identifies a note and then extracts medication entities.
What you need first
- Python 3.10+ and pip
- Optional: Apple Silicon for MLX, Android with ONNX Runtime Mobile, or a browser with WebGPU
- Model downloads from the Hugging Face Hub, or local model folders for offline use
Steps
- Run
git clone https://github.com/maziyarpanahi/openmed && cd openmed - Run
./install-skills.shto install the skills for Claude Code, Codex and OpenCode - Ask: Build a local OpenMed pipeline that de-identifies a synthetic discharge note and extracts medication entities
- Let the agent produce the Openmed.deidentify then Openmed.analyze_text pipeline
- Run it once to download the models, then run it offline
Prompt for your agent
Using the maziyarpanahi/openmed Agent Skills, build a local pipeline that de-identifies <synthetic note> and extracts medication entities, then show the output and the models it used.
Caveat. This card is written from the maziyarpanahi/openmed README, not from a test run by Deduped. Before you start, make sure you have Python 3.10+ and pip. Check the repo for newer instructions before you rely on these steps.
Use this in your AI agent
Ask your agent
Using the Deduped MCP, get the setup steps for maziyarpanahi/openmed and "Have your coding agent build a de-identification pipeline with OpenMed skills"
Add Deduped to Claude Code once
claude mcp add --transport http deduped https://dedupedai.com/mcp
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