pyhealth

PyHealth

Targets PyHealth 2.0.2, verified against its released wheel, current official documentation, and CPU execution on Python 3.12/Torch 2.7.1. PyHealth's pipeline is Dataset -> Task -> Model -> Trainer -> Metrics; its 1.x and 2.x interfaces differ. Do not combine legacy Visit examples with the 2.x event/processor API.

When to use

Use for clinical prediction with PyHealth, including EHR sequences, physiological signals, imaging tasks, or medical-code lookup. Establish the cohort, prediction time, observation window, outcome horizon, and unit of evaluation before modeling. For general tabular learning without a PyHealth dataset/task, this skill is optional.

Install and smoke-test

uv run --no-project --isolated --python 3.12 --with pyhealth==2.0.2 python assets/starter_pipeline.py --demo --epochs 1

Run that command from the skill directory, or use the absolute path to the asset. It trains on invented in-memory records and exercises patient splitting, metrics, and best-checkpoint restoration. It provides no evidence of clinical performance. See installation for project setup and device options.

Workflow

1. Inspect the installed version and dataset configuration. Both MIMIC-III and MIMIC-IV use lowercase table selectors in 2.0.2. MIMIC-III files remain uppercase.
2. Check task semantics and required tables. MortalityPredictionMIMIC3 predicts death in the next admission, excludes the last admission, and requires diagnoses, procedures and prescriptions in the current admission. It is not a current-stay early-warning model. Missing/invalid next-admission mortality flags are assigned zero upstream; audit this before using real data.
3. Create supervised samples. base.set_task(task) returns a processed dataset with input/output schemas. Inspect raw task output as well as processed samples.
4. Partition by patient and verify overlap, class counts and observation windows. split_by_patient(..., seed=42) is random, not chronological or stratified. It does not prevent within-visit temporal leakage or preprocessing leakage.
5. Choose a schema-compatible model and run one batch before training. Construct it from the training sample dataset. Transformer uses embedding_dim, not hidden_dim; arguments are model-specific.
6. Declare validation metrics and the exact monitor. Supply metrics=[...] to Trainer; the monitor must be a returned key. An absent key raises an error. Use monitor_criterion="min" for loss, "max" for AUC/accuracy.
7. Evaluate the held-out test set once the model choice is fixed. Report prevalence, patient counts, discrimination, calibration, threshold policy and uncertainty as appropriate. PR-AUC's no-skill reference depends on prevalence, not a universal 0.5.