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mkurman/zorai/openmed

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version: "1.0.0" name: openmed description: "Production-ready medical NLP toolkit (maziyarpanahi/openmed). Entity extraction, assertion detection, PII de-identification, batch processing, REST API, and multilingual support. Covers installation, all model families, configuration, and deployment." tags: [medical-nlp, clinical-text, entity-extraction, deidentification, pii, icd10, healthcare, zorai, openmed]

OpenMed

OpenMed transforms clinical text into structured insights. It bundles curated biomedical NER models, HIPAA-compliant de-identification, batch processing, a Dockerized REST API, and Apple Silicon acceleration — all behind a single analyze_text() call.

When to Use

Scenario	Start with
Extract diseases, drugs, anatomy from clinical notes	`references/entity-extraction`
Remove PHI/PII before sharing or storing data	`references/pii-deidentification`
Run NER on hundreds of clinical documents	`references/batch-processing`
Serve OpenMed behind a REST API	`references/rest-service`
Set up on Apple Silicon, Docker, or Swift	`references/installation`
Configure profiles, pick the right model	`references/configuration`
PII in French, German, Spanish, Portuguese, etc.	`references/multilingual-pii`
Privacy Filter (OpenAI / Nemotron) families	`references/privacy-filter`

Quick Start

bash

git clone https://github.com/maziyarpanahi/openmed.git
cd openmed
uv pip install -e ".[hf]"

python

from openmed import analyze_text
result = analyze_text(
    "Patient started imatinib for chronic myeloid leukemia.",
    model_name="disease_detection_superclinical",
)
for entity in result.entities:
    print(f"{entity.label:<12} {entity.text:<35} {entity.confidence:.2f}")
# DISEASE      chronic myeloid leukemia          0.98
# DRUG         imatinib                           0.95

Model Registry (12+ Models)

Model	Entity Types
`disease_detection_superclinical`	DISEASE, CONDITION, DIAGNOSIS
`pharma_detection_superclinical`	DRUG, MEDICATION, TREATMENT
`pii_detection_superclinical`	NAME, DATE, SSN, PHONE, EMAIL, ADDRESS
`anatomy_detection_electramed`	ANATOMY, ORGAN, BODY_PART
`gene_detection_genecorpus`	GENE, PROTEIN

Browse the full catalog: openmed.life/docs/model-registry

Key Concepts

analyze_text() — single-call inference with configurable model, aggregation, format, and confidence threshold
BatchProcessor — multi-text and multi-file workflows with progress tracking
extract_pii() / deidentify() — HIPAA-compliant PII detection and redaction
Configuration Profiles — dev, prod, test, fast presets via YAML or env vars
REST API — FastAPI endpoints: /health, /analyze, /pii/extract, /pii/deidentify

References

OpenMed docs
OpenMed arXiv paper
OpenMed GitHub
references/installation.md — cross-platform install, Docker, Swift
references/entity-extraction.md — disease, drug, anatomy, gene models
references/pii-deidentification.md — HIPAA compliance, smart merging, anonymization
references/batch-processing.md — BatchProcessor API
references/rest-service.md — FastAPI endpoints, Docker
references/configuration.md — profiles, model registry, profiling
references/multilingual-pii.md — 9-language PII support
references/privacy-filter.md — OpenAI Privacy Filter, Nemotron, MLX

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