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name: medical-code-text description: "Guides PyHealth medical-code ontology lookup and mapping plus NLP, text, vision, audio, EEG, signal, and multimodal processing with explicit optional-resource gates." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Medical code, text, and multimodal workflows

Use this route for ICD/CCS/ATC/NDC/RxNorm/UMLS lookup or mapping, clinical text and NLP, text/image/audio/signal processors, or multimodal embeddings.

Workflow

1. Identify the code vocabulary or modality and whether local mapping/model assets already exist. Read code maps or NLP/multimodal.
2. Run scripts/optional_dependency_probe.py without downloading anything. Install [nlp] for NLP extras and [graph] for graph paths only when needed.
3. Use InnerMap.load(vocabulary) for ontology lookup or CrossMap.load(source_vocabulary, target_vocabulary) for cross-system maps; validate code-system names and cache/network policy first.
4. Distinguish local tokenization/processor checks from pretrained model weight acquisition. Route model orchestration to models-training and generic processor/data schemas to data-pipelines.
5. For a full multimodal experiment, record each modality's processor output, time/mask semantics, encoder, alignment, missing-modality policy, and device.

The bundled medcode smoke performs only explicit local/API checks; it does not fetch mappings, corpora, credentials, or model weights. Read troubleshooting and long-tail for gated capabilities.