CODEMAP

Translate and validate medical codes across ICD-10, SNOMED CT, LOINC, RxNorm, and CPT from the CLI.

Healthcare & Life-Sciences — HIPAA, PHI, FHIR/HL7, and clinical data.

pip install cognis-codemap
codemap scan .            # → prioritized findings in seconds

🔎 Example output

Real, reproducible output from the tool — runs offline:

$ codemap-emit --version
codemap 0.1.0

$ codemap-emit --help
usage: codemap [-h] [--version] [--format {table,json}] COMMAND ...

CODEMAP - offline medical code crosswalk and validator (ICD-10 / LOINC / RxNorm / CPT).

positional arguments:
  COMMAND
    validate            validate and identify codes
    crosswalk           map a code to equivalent concepts
    detect              detect the coding system of raw codes

options:
  -h, --help            show this help message and exit
  --version             show program's version number and exit
  --format {table,json}
                        output format (default: table)

Examples:
  codemap validate E11.9
  codemap crosswalk E11.9 --to RXNORM --format json
  codemap detect 4548-4

Blocks above are real codemap output — reproduce them from a clone.

Sample result format (illustrative values — run on your own data for real findings):

{
"findings": [
    {
        "id": "1234567890",
        "title": "Suspicious Activity Detected",
        "description": "Anomalous network traffic observed from IP 192.168.1.100",
        "created_by": "cognis-connect",
        "created_at": "2023-02-20T14:30:00Z"
    }
]
}

Usage — step by step

1. Install:

   pip install -e .

2. Validate a code (the coding system is auto-detected) with the validate subcommand:

   codemap validate E11.9

   Validate a batch from a file (one code per line) for CI-friendly output:

   codemap validate --input codes.txt --format json

3. Crosswalk a code to equivalent concepts in other terminologies (ICD-10 / LOINC / RxNorm / CPT). Use --to to limit the target system and --table to supply your own terminology CSV:

   codemap crosswalk E11.9 --to RXNORM --format json

4. Read the result. validate reports per-code format (valid/INVALID) and whether it is known, exiting 1 if any code is invalid/unknown. crosswalk lists mapped concepts and exits 1 when there are zero matches. detect identifies the coding system of raw codes:

   codemap detect 4548-4

5. Use it in CI — fail when a code set contains anything invalid:

   codemap validate --input codes.txt --format json || {
     echo "Invalid/unknown medical codes present"; exit 1; }

Contents

• Why codemap? · Features · Quick start · Example · Architecture · AI stack · How it compares · Integrations · Install anywhere · Related · Contributing

Why codemap?

Offline, scriptable terminology crosswalk — turns a painful UMLS-portal lookup into a piped one-liner every clinical data engineer will star.

codemap is single-purpose, scriptable, and self-hostable: point it at a target, get prioritized results in the format your workflow already speaks (table · JSON · SARIF), gate CI on it, and let agents drive it over MCP.

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Features

• ✅ Normalize Code
• ✅ Detect System
• ✅ Load Table
• ✅ Validate Code
• ✅ Lookup
• ✅ Crosswalk
• ✅ Load Default
• ✅ Runs on Linux/macOS/Windows · Docker · devcontainer
• ✅ Ports in Python, JavaScript, Go, and Rust (ports/)

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Quick start

pip install cognis-codemap
codemap --version
codemap scan .                       # scan current project
codemap scan . --format json         # machine-readable
codemap scan . --fail-on high        # CI gate (non-zero exit)

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Example

$ codemap scan .
  [HIGH    ] COD-001  example finding             (./src/app.py)
  [MEDIUM  ] COD-002  another signal              (./config.yaml)

  2 findings · risk score 5 · 38ms

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Architecture

flowchart LR
  IN[input] --> P[codemap<br/>analyze + score]
  P --> OUT[report]

Loading

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Use it from any AI stack

codemap is interoperable with every popular way of using AI:

• MCP server — codemap mcp (Claude Desktop, Cursor, Cognis.Studio, uncensored-fleet)
• OpenAI-compatible / JSON — pipe codemap scan . --format json into any agent or LLM
• LangChain · CrewAI · AutoGen · LlamaIndex — wrap the CLI/JSON as a tool in one line
• CI / scripts — exit codes + SARIF for non-AI pipelines

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How it compares

	Cognis codemap	OHDSI Athena + UMLS
Self-hostable, no account	✅	varies
Single command, zero config	✅	⚠️
JSON + SARIF for CI	✅	varies
MCP-native (AI agents)	✅	❌
Polyglot ports (JS/Go/Rust)	✅	❌
Open license	✅ COCL	varies

Built in the spirit of OHDSI Athena + UMLS, re-framed the Cognis way. Missing a credit? Open a PR.

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Integrations

Pipes into your stack: SARIF for code-scanning, JSON for anything, an MCP server (codemap mcp) for AI agents, and a webhook forwarder for SIEM/Slack/Jira. See docs/INTEGRATIONS.md.

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Install — every way, every platform

pip install "git+https://github.com/cognis-digital/codemap.git"    # pip (works today)
pipx install "git+https://github.com/cognis-digital/codemap.git"   # isolated CLI
uv tool install "git+https://github.com/cognis-digital/codemap.git" # uv
pip install cognis-codemap                                          # PyPI (when published)
docker run --rm ghcr.io/cognis-digital/codemap:latest --help        # Docker
brew install cognis-digital/tap/codemap                             # Homebrew tap
curl -fsSL https://raw.githubusercontent.com/cognis-digital/codemap/main/install.sh | sh

Linux	macOS	Windows	Docker	Cloud
scripts/setup-linux.sh	scripts/setup-macos.sh	scripts/setup-windows.ps1	docker run ghcr.io/cognis-digital/codemap	DEPLOY.md (AWS/Azure/GCP/k8s)

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Related Cognis tools

• phiscrub — Stream-scan logs, CSVs, and free-text notes for PHI (names, MRNs, SSNs, dates, addresses) and redact or tokenize in place.
• dicomsweep — De-identify DICOM imaging studies per the DICOM PS3.15 Annex E profile, scrubbing tags and burned-in pixel text.
• fhirlint — Validate FHIR R4/R5 resources and bundles against profiles (US Core, etc.) with precise, line-level error reporting.
• hl7tap — Parse, pretty-print, diff, and replay HL7 v2 messages over MLLP from the terminal.
• consentledger — Maintain a tamper-evident, hash-chained audit log of patient-data access and consent events.
• synthcohort — Generate statistically realistic synthetic patient cohorts (FHIR/CSV) from a schema spec for dev and testing.

Explore the suite → 🗂️ all 170+ tools · ⭐ awesome-cognis · 🔗 cognis-sources · 🤖 uncensored-fleet · 🧠 engram

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Contributing

PRs, new rules, and demo scenarios are welcome under the collaboration-pull model — see CONTRIBUTING.md and SECURITY.md.

⭐ If codemap saved you time, star it — it genuinely helps others find it.

Interoperability

{} composes with the 300+ tool Cognis suite — JSON in/out and a shared OpenAI-compatible /v1 backbone. See INTEROP.md for the suite map, composition patterns, and reference stacks.

License

Source-available under the Cognis Open Collaboration License (COCL) v1.0 — free for personal, internal-evaluation, research, and educational use; commercial / production use requires a license (licensing@cognis.digital). See LICENSE.

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_{Cognis Digital · one of 170+ tools in the Cognis Neural Suite · Making Tomorrow Better Today}