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name: clinical-tasks description: "Guides PyHealth task selection, task-to-dataset attachment, clinical label and feature contracts, patient-safe sample splits, and bounded custom task definitions." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

PyHealth clinical tasks

Use this route when the user asks for mortality, readmission, length-of-stay, drug recommendation, coding, survival, de-identification, EEG/CXR/variant, patient-linkage, or another task-specific sample pipeline.

Workflow

1. Identify the source dataset and task family; read task catalog for current public exports and data prerequisites.
2. Construct the dataset in data-pipelines, then instantiate the task and call dataset.set_task(task).
3. Inspect one task sample: identity fields, feature keys, labels/targets, dtypes, and processor aliases. Use scripts/validate_task_samples.py for a local JSON contract before invoking a model.
4. Split by patient and route model/vocabulary compatibility to models-training. Route metric semantics to evaluation-interpretability.
5. For a new task, subclass BaseTask, define task_name, input_schema, output_schema, and __call__(patient), and optionally implement pre_filter or pass code mappings. Test on synthetic patients first.

Credentialed MIMIC/eICU/OMOP/PhysioNet/MEDS data and long benchmark examples are not package-import tests. Preserve access and schema gates explicitly. Read workflows, API details, and troubleshooting.