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name: pyhealth description: "Guides PyHealth 2.0 healthcare-AI workflows for clinical datasets, task construction, preprocessing, models, training, evaluation, medical-code mapping, and multimodal data." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

PyHealth

Use this skill when a task involves the pyhealth Python package or a healthcare-AI pipeline built from structured EHR, clinical text, medical codes, images, signals, or multimodal samples. PyHealth 2.0 organizes work as: dataset → task → processor/sample → model → trainer → metrics. Keep patient identity out of train/validation/test overlap unless the experiment explicitly requires another protocol.

First route

1. Check the Python/package prerequisite and resource gates in installation-and-environment.
2. Decide whether the request is primarily data preparation, task definition, model training, evaluation, or medical-code/text/multimodal work.
3. Read exactly one focused route first:
   • data-pipelines for dataset classes, schemas, processors, splits, loaders, and local fixtures.
   • clinical-tasks for built-in tasks, labels, feature keys, and custom task classes.
   • models-training for model selection, dataset contracts, Trainer, devices, checkpoints, and inference.
   • evaluation-interpretability for metrics, calibration, prediction sets, and explanations.
   • medical-code-text for InnerMap, CrossMap, NLP, code/text/vision/audio/signal, and multimodal routes.
4. For a complete experiment, follow the links between routes rather than copying an old example verbatim. Several public examples target PyHealth 1.x names; prefer the current APIs documented in the focused route.

Minimal checks

After installing the package, verify the public import before accessing a clinical dataset:

python -c "import pyhealth; print('PyHealth import ok')"
python -c "from pyhealth.datasets import SampleDataset, split_by_patient, get_dataloader; from pyhealth.trainer import Trainer; print('core API ok')"

The package metadata requires Python >=3.12,<3.14. Install the base package with pip install pyhealth; use pip install 'pyhealth[graph]' for PyTorch-Geometric paths and pip install 'pyhealth[nlp]' for NLP metrics and fuzzy matching. Use a compatible PyTorch build for the requested device; a CPU import does not prove CUDA behavior.

Safety and evidence gates

Do not download MIMIC, eICU, OMOP, PhysioNet, MEDS, model-hub weights, mapping caches, or NLTK corpora unless the user has supplied authorization, access, and a bounded destination. Do not put PHI or credentials in generated fixtures. Use synthetic/local fixtures for API and schema checks. Treat training, benchmark, and notebook-scale examples as recipes until data size, runtime, and device are explicitly bounded.

Read troubleshooting for cross-cutting import, optional-dependency, data-access, device, and legacy-example failures. Read repo-provenance before deciding whether this skill matches a changed PyHealth checkout.